Upping the Game: How 2D U-Net Skip Connections Flip 3D Segmentation
Authors: Xingru Huang
Yihao Guo
Jian Huang
Tianyun Zhang
HE HONG
Shaowei Jiang
Yaoqi Sun
Venue: NeurIPS 2024 poster (2024)
Keywords: 3D medical image segmentation
Anisotropic voxel spacing
Skip connection
Plane feature extraction
Multiscale feature fusion
Primary area: machine_learning_for_healthcare
TL;DR: We propose 2D U-Net skip connection (uC) to replace conventional skip connections in 3D segmentation networks.

Abstract: In the present study, we introduce an innovative structure for 3D medical image segmentation that effectively integrates 2D U-Net-derived skip connections into the architecture of 3D convolutional neural networks (3D CNNs). Conventional 3D segmentation techniques predominantly depend on isotropic 3D convolutions for the extraction of volumetric features, which frequently engenders inefficiencies due to the varying information density across the three orthogonal axes in medical imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). This disparity leads to a decline in axial-slice plane feature extraction efficiency, with slice plane features being comparatively underutilized relative to features in the time-axial. To address this issue, we introduce the U-shaped Connection (uC), utilizing simplified 2D U-Net in place of standard skip connections to augment the extraction of the axial-slice plane features while concurrently preserving the volumetric context afforded by 3D convolutions. Based on uC, we further present uC 3DU-Net, an enhanced 3D U-Net backbone that integrates the uC approach to facilitate optimal axial-slice plane feature utilization. Through rigorous experimental validation on five publicly accessible datasets—FLARE2021, OIMHS, FeTA2021, AbdomenCT-1K, and BTCV, the proposed method surpasses contemporary state-of-the-art models. Notably, this performance is achieved while reducing the number of parameters and computational complexity. This investigation underscores the efficacy of incorporating 2D convolutions within the framework of 3D CNNs to overcome the intrinsic limitations of volumetric segmentation, thereby potentially expanding the frontiers of medical image analysis. Our implementation is available at https://github.com/IMOP-lab/U-Shaped-Connection.

Upping the Game: How 2D U-Net Skip Connections Flip 3D Segmentation
Xingru Huang'; Yihao Guo Jian Huang'; Tianyun Zhang',
Hong He Shaowei Jiang' Yaoqi Sun Hannzzhuu Dianzi University
Abstract
In the present study, we introduce an innovative structure for 3D medical image segmentation that effectively integrates 2D U-Net-derived skip connections into the architecture of 3D convolutional neural networks (3D CNNs). Conventional 3D segmentation techniques predominantly depend on isotropic 3D convolutions for the extraction of volumetric features, which frequently engenders inefficiencies due the varying information density across the three orthogonal axes medical imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). This disparity leads to decline in axial-slice plane feature extrac- tion efficiency, with slice plane features being comparatively underutilized relative to features in the time-axial. To address this issue, we introduce the U-shaped Connection (uC), utilizing simplified 2D U-Net in place of standard skip connec- tions to augment the extraction of the axial-slice plane features while concurrently preserving the volumetric context afforded by 3D convolutions. Based uC, we further present uC 3DU-Net, an enhanced 3D U-Net backbone that integrates the uC approach to facilitate optimal axial-slice plane feature utilization. Through rig- orous experimental validation on five publicly accessible Satiisttt-FLAREE22222 OIMHS, FeTA2221, AbdomenCT-11K. and BTCV, the proposed method surpasses contemporary state-of-the- models. Notably, this performance is achieved while reducing the number of parameters and computational complexity. This inves- tigation underscores the efficacy of incorporating 2D convolutions within the framework of 3D CNNs to overcome the intrinsic limitations of volumetric segmen- tation, thereby potentially expanding the frontiers of medical image analysis. Our implementation is available at https://github.com/ httptiuu..oomMMMMMPLL..... Lao/--la/UU-haapppp Uhappedd-------h Chaped-Connection
1 Introduction
3D volumetric data segmentation extensively relies on the utilization of axial symmetrical 3D convolutions to extract features based volumetric representation. Imaging modalities such as CT [1, 2, 3, 4] and MRI [S, 6, 7, 8, 9] yield high-precision images along the slice plane and repeat this process along the temporal axis, resulting in non-uniform information density across the three axes. We visualize the difference in information density between the axial and the slice plane in 3D medical imaging in Fig. 1. The information density variance arising from non-simultaneous imaging in three dimensions of volumetric data engenders distinctiveness between time-axial and slice plane features, rendering them unsuitable for the same processing. Typically, features extracted from the slice plane possess higher information density and are more adept at delineating the fine- grained, voxel-level neighboring structures. These localized features are essential in understanding the precise 3D structure of tissues, thereby surpassing the utility of time-axial features. However,
'These authors contributed equally.
1 *Coresponding author. Email: hehong @ whhducdu.com jiangsw @ wadd.eddu.enn syq hdu.edu.en
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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(a)
(b)
(C)
Figure 1: Visualization of the difference in information density between the time-axial (Green) and slice planes (Red). Panels (a), (b), and (c) respectively illustrate the information density differences in time-axial and slice planes for volumetric data of the abdomen, retina, and brain tissues.
traditional 3D convolutions [10. 11, 12), although excelling in time-axial information extraction, significantly increase computational complexity to accommodate time-axial information, leading to axial-slice plane performance drop-off in 3D CNNs. Thus. the structural constraints of 3D convolutions inherently weaken the rich local features of the slice plane. When confronted with sequences abundant slice plane features, symmetric 3D convolutions face substantial inefficiencies and performance degradation.
To address the axial-slice plane performance drop-off issue, efficient strategy involves increasing channel depth to enhance feature extraction performance. The depth of the channels, indicative of the high-dimensional feature richness, directly influences network performance. Increasing channel depth can enhance the network's 's capacity to capture more high-dimensional information and improve feature representation quality. However, the relationship between channel depth and network performance in 3D CNNs is intricate and nonlinear. While initially increasing channel depth improves model performance by introducing more detailed and abstract features, this improvement comes at the expense of ever-increasing computational overhead. Moreover, an excessive number of channel depths can complicate the model's 's information processing, potentially introducing noise and redundancy, culminating in dimensional explosion and performance decline. The linear relationship between channel depths and computational overhead means that increasing channel depth significantly inflates computational costs, leading to reduced model efficiency under identical hardware constraints. Conversely, 2D convolutions, with their focus on slice-wise information extraction, exhibit higher efficiency and performance peaks, boasting superior parameter performance ratio. For kernel size K, a 2D convolution requires only K of the parameters compared to its 3D counterpart, thereby significantly enhancing local feature extraction efficiency within slice. Consequently, ratio of 3D CNÑ architectures, markedly increasing axial-slice plane feature extraction efficiency while minimizing parameter count and computational load.
2D convolutions present a key solution to the low coanetterto--erforne
Given the excellent computational efficiency and parameter-to-perfooree for axial-slice plane feature utilization, and considering the prevalent use of skip connections in 31) medical image segmentation architectures to integrate early layer details, propose employing 2D convolutions within skip connections to supplement axial-slice plane information. For this purpose, we introduce a plug-and-play U-shaped Connection (uC), leveraging a simplified 2D U-Net replace skip connections in 3D segmentation architectures for improving the utilization efficiency of axial- slice plane features. Using classical 3D U-Net the backbone, we further propose the uC 3DU-Net, which substitutes the original skip connections with uC, thereby enhancing the network's ability to comprehend slice plane information and efficiently utilize initial encoded layer details. For more effective integration of 3D spatial features from 3D CNNs and 2D slice plane features introduced by uC, we employ Dual Feature Integration (DFi) module to combine
ratio of 2D convolutions
titiiiiimmniinall features.
Experimental evaluations demonstrate the incorporation of uC significantly enhances segmentation performance even with reduced channels and surpasses parameter-fo parameter-o--prffrmaec ratios of state-of- the-art networks. Comparative analyses on five publicly available datasets FF.NNF20222111111 OIMHS, FeTA2021, AbdomenCT-IK. and BorCV-corroborat the superiority of proposed uC 3DU-Net over existing fate--o-the-ann methods, achieving persuasive performance with reduced computational complexity and model parameters.
The principal contributions of our work can be summarized as follows:

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1. We proposed UC, utilizing simplified 2D U-Net to replace traditional skip connections in 3D segmentation backbones, enhancing model capacity to capture axial-slice plane features.
2. uC 3DU-Net is further proposed, adopting uC to replace original skip connections, and applying the DFi module to effectively merge 3D sequential spatial features with 2D axial-slice plane features, thus improving feature utilization efficiency.
3. We explore the complementary relationship between uC and 3D CNNs, revealing that incorporating 2d convolutions in 3D CNNs can achieve superior performance with fewer parameters, striking perfect balances between efficiency and performance in volumetric segmentation.
4. Comparative experiments on five public datasets=FLARRE2211 OIMHS, FeTA2021, AbdomenCT- performance with fewer parameters and lower computational cost.
KK. and VTCV-demonstrale that uC 3DU-Net surpasses all previous models, achieving SOTA
2 Related Work
2.1 3D medical image segmentation
The realm of 3D medical image segmentation has experienced substantial advancements, primarily propelled by the progressive evolution of deep learning paradigms and the augmentation of computa- tional prowess [13, 14]. At the key of these advancements lies the continuous enhancement of CNNs [15, 16, 17, 18, 19] and the advent of Vision Transformers (ViTs) [20]. both of which have signifi- cantly promoted segmentation methodologies [21, 22, 23, 24, 25]. The 3D U-Net [26]. a foundational model in this field, the subsequent iterations have incorporated attention mechanisms, augmenting feature extraction capabilities by prioritizing important regions while mitigating noise interference [27, 28). Transformer-basee architectures, exemplified by the UNETR framework [29, 30], leverage multi-head self-attention mechanisms to capture long-range dependencies, thus facilitating more precise and meticulous segmentation. Hybrid models that combine CNNs with Transformers, such as TrensUNet [31] and TransBTS [32], effectively balance local spatial feature extraction with global contextual understanding, culminating in superior segmentation performance [33, 34]. The nnU-Net [35], an exemplary self-adaptive framework, has demonstrated exceptional performance across a diverse array of medical imaging tasks by autonomously configuring itself to specific datasets.
Beyond architectural innovations, multitude of research endeavors have concentrated on refining 3D medical image segmentation mask techniques. Chen et al. [36] propose novel method integrating Active Appearance Models (AAM) with live wire (I.W) and Graph Cuts (GC) techniques, thereby enhancing the efficacy of 3D medical image segmentation. Zhang et al. [21] develop the 3D Context Residual Network (ConResNet), which employs context residual module interlink the segmentation decoder with the context residual decoder, explicitly learning inter-slice contextual information improve segmentation accuracy. Advanced loss functions and optimization strategies, such as extensive implementations of Dice loss [37], have been designed to address class imbalance issues, ensuring more balanced learning process between easy and hard examples. Furthermore, data augmentation and transfer learning techniques have been beneficial in enhancing model robustness and generalization capabilities [38, 39j. These unremitting innovations promise increasingly precise and reliable analysis of 3D medical images, significantly advancing the fields of medical diagnostics and therapeutic planning.
2.2 Advancements in skip connection structures
Innovations in skip connection structures have further augmented the performance and computational efficiency of CNNs [40, 41, 42] in the domain of medical image segmentation. Skip connections are important in facilitating direct gradient propagation, thereby mitigating the vanishing gradient issue and enabling the training of substantially deeper networks. This method permits unimpeded gradient flow throughout the network, effectively cireumventing intermediate layers, minimizing information degradation, and achieving more stable and efficient optimization. Prominently exemplified in the ResNet[43] architecture, skip connections mitigate the vanishing gradient dilemma, thereby fostering the training of deep neural networks. In the U-Net[44] architecture, skip connections are crucial in the transmission of initial detailed spatial information from the encoder to the decoder, thereby preserving high-resolution details indispensable for precise segmentation tasks.
3

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Recent endeavors have extensively refined traditional skip connections to augment their functional efficacy. Attention U-Net [45] replaces the normal application of skip connections by incorporating attention mechanisms that dynamically emphasize important features while attenuating irrelevant information. Dense U-Net [46] leverages densely connected convolutional networks (DenseNet) [47] to facilitate feature reuse and improve gradient flow. Interconnecting each layer with every other layer a feed-forwart manner facilitates the assimilation of richer and more diverse feature representations. Skip connections in the Residual Channel Attention Network (RCAN) [48] network mitigate the training difficulty of deep neural networks and enhance feature reuse and information flow efficiency, thereby improving the network's ability to learn feghfreuunncy information. Hybrid Densely Connected UNet (H-DenseUNee [49] incorporates skip connections within dense blocks, combining the strengths of DenseNet and U-Net. By harnessing advanced feature reuse, optimized gradient flow facilitated by dense connectivity, and efficient multi-scale feature fusion, it produces a robust architecture adept at capturing intricate details and nuanced contextual information.
3 Method
3.1 Preliminaries: 3D convolutional segmentation networks and skip connections
Current 3D medical image segmentation networks primarily utilize architectures based on 3D convolutions, as exemplified by classic models such as 3D U-Net, SegResNet, and the recent 31) UX-Net. These networks adopt an end-to-end encoder-decode framework with skip connections. The encoder progressively increases channel depth while downsampling spatial feature maps to abstract and consolidate contextual information, thereby reducing parameter complexity and improving computational efficiency. In contrast, the decoder gradually upsamples these encoded features to restore the original resolution, refining segmentation boundaries and overall segmentation accuracy. Skip connections effectively reintroduce ane-ggrined details from the original images during the upsampling stage, aiding the refinement of segmentation results.
Several 3D medical image segmentation networks, such as UNETR, TransBTS, and SWinUNETR. have incorporated multi-head self oelf-attention and advanced skip connections within the 3D Conv-based ricoder-decoder framework. These hybrid approaches effectively capture long-range dependencies and facilitate multi-scale feature utilization. Multi-head self-attention ensures global consistency, while 3D convolution operations excel in preserving local spatial details. The combination of self-attention with 3D convolutions ensures the model's ability to retain image details while compre- hending and processing global information more effectively. However, relying solely on transformer architectures, such as Vision Transformers, would result in a substantial increase in parameters and computational load. Additionally, without skip connections, is difficult to achieve satisfactory segmentation details. Hence, 3D convolutions and skip connections remain crucial for achieving optimal segmentation performance in 3D medical image segmentation.
3.2 U-shaped Connection
Considering the objective of skip connections to capture detailed features of original images, it is essential to note that 3D medical images fundamentally represent a sequence of 2D images. Given the anisotropic nature of medical images, the 2D slice plane shows richer feature information compared to the temporal sequence axis. Although basic skip connection methods like cat and addition can supplement original feature information to some extent, these methods fail to fully utilize the rich axial-slice plane information in 3D medical images. To address the challenges posed by the anisotropy of 3D medical images, the U-shaped Connection(uC) is proposed. It offers a solution to anisotropy in medical imaging. This approach employs a simplified 2D U-Net implement skip connections. thereby supplementing the rich original 2D slice plane feature information, and enhancing the 2D slice plane feature extraction capabilities of any 3D image segmentation network. The uC combines features extracted by 2D convolution with those extracted by 3D upsampling, offering more efficient feature extraction compared to pure 3D convolution, as detailed structure in Sec3.3.
The fundamental structure of uC is based on a 2D U-Net. In comparison to the basic 2D U-Net, uC omits the initial and final conv layers, retaining only the downsampling and upsampling layers to minimize computational load and enhance the extraction efficiency of original slice plane features. Each downsampling layer comprises an average pooling layer and two conv layers, each consisting of 2D conv, Group Normalization (GN), and ReLU. The average pooling layer reduces the feature map size by half, and the subsequent conv layers double the channel number, with the group number of GN set to half the current channel number. Each upsampling layer includes transposed convolution layer and two conv layers, where the transposed convolution layer restores the feature map to its original size, and the cony layers reduce the channel number back to its half.

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Figure 2: Overview of the proposed uC 3DU-Net architecture. The backbone a 3D U-Net with five encoding-decoding stages and each downsampling block comprises max-pooling layer followed by two 3D convolutional layers. In stages I to 3, 5D tensors are rearranged into 4D tensors for 2D uC input by stacking slices along the batch dimension. Each upsampling layer employs transposed convolution to upsampling and a DFi block to effectively adjust the feature channel depth.
3.3 uC 3DU-Net
Based on uC, we proposed uC 3DU-Net, which employs the 3D U-Net as the backbone, incorporating uC with a dual feature integration (DFi) module. The network architecture is illustrated in Fig. Given the input 3D volumetric image data denoted z, divide the encoder of the 3D U-Net into five stages. Stage is the initial Two Conv layer, comprising two 3D convolutions, expanding the input feature channel depth to 24. Stages 2 to 5 are downsampling layers, each comprising max- max-pooling operation and two 3D convolutions, doubling the feature channels while halving the feature map dimensions. The outputs of stages to 5 are denoted as (6:1)) to (0:5)) The decoder also divided into five stages. Stages 5 to 2 utilize transposed convolutions upsample, effectively doubling the spatial dimensions of the feature maps. Stages 5 to 3 apply DFi to effectively halve the feature channel depth, while Stage 2 retains two convolution layers. Stage is the Final Conv layer, converting the feature channels to the number of categories by a 3D convolution. All 3D convolution operations subsequently followed by Instance Normalization and LeakyReLU activation function, with the negative slope set to 0.1.
Considering the parameter--fficiency trade-off, we employ the uC in only stages to 3, denoted as uC1 to uC3. The inputs x! (9:1) to (9:3) are reshaped into 4D dimensions by stacking slices along the batch dimension using a rearrange operation, resulting in (((1)) to ((:3). The structure of uCi to UC3 resembles that of simplified version of 2D U-Net, with detailed descriptions of the uC architecture provided in Section 3.2. uC1, uC2, and uC'3 consist of 4, 3, and 2 downsampling and upsampling layers, respectively, to match the dimensions of the input 4D tensor. The outputs of (0:1) to ((33) after passing through the uC are ((0:1) to (,(:3), which are reshaped back to 5D dimensions using a rearrange operation, resulting ((6:1) to ((d:3).
We utilize the dual feature integration (DFi) module to seamlessly integrate the 3D spatial features extracted by the 3D CNN with the rich axial-slice plane features introduced by the uC. Specifically, (=(:4) and (0:5) are fused using the DFi block to obtain z" x" and ((d:3) are fused to obtain x". and z and (():)) are fused to obtain Iτ, Finally, = and g(0:1) are fused using the DFi block and undergo the final convolutional block to obtain the final output y.
Dual Feature Integration. In conventional 3D U-Net architectures, skip connections integrate the original feature information from the encoder during the upsampling process through cat, followed by two 3x3 convolutions to reduce the number of channels and consolidate useful information. However, this basic feature fusion approach may lead to inefficiencies when reconciling the disparity between 3D spatial features and 2D slice plane features. Therefore, we devise streamlined, efficient multi-scale feature fusion module DFi to better integrate the axial-slice plane information introduced by uC. The 2D slice plane features extracted by uC and the 3D spatial features from the 3D CNN
5

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encoder are cat to retain maximal original feature information. However, subsequent convolution operations to reduce channel depths are computationally intensive and inefficient. Utilizing addition for feature fusion could result in information loss due to inherent discrepancies between the two types of features. Hence, an approach combining cat, addition, and subsequent convolution layers, with the aim of minimizing parameter count and FLOPs, resulting in the design of the DFi module. For the 2D slice plane features FI extracted by uC and the 3D spatial features F2 obtained during 3D U-Net upsampling, branch of the DFi module cats FI and F2, then employs 1xl conv to halve the channel count, preserving important features to produce feature map Fc. Branch 2 initially extracts critical information from F1 and F2 using 1xl convs, followed by addition and sigmoid activation to generate the attention map Fa. The element- wise multiplication of Fc and Fa adjusts and integrates the feature map Fc from branch 1, thereby learning the relative importance of Fl and F2 during the fusion process. The proposed DFi module integrates and simplifies addition, cat, and subsequent convolution layers, achieving more lightweight and efficient fusion of 2D and 3D spatial features.
4 Experiments and results
4.1 Datasets
To validate the efficacy of the proposed method, multiple experiments are conducted on five widely- used, publicly accessible datasets: FLARE2021 [50], OIMHS [51], FeTA2021 [52], AbdomenCT- IK [53], and BTCV [54]. To ensure experimental rigor and fairness, apply identical data preprocessing and hyperparameter settings across all datasets, uniformly utilizing the D for training.
FLARE2021. An anisotropic CT dataset dedicated to abdominal organ segmentation comprises 361 training instances, 50 validation instances, and 100 test instances, categorized into four organ classes: spleen, kidney, liver, and pancreas. The spatial resolution ranges from 0.61 mm to 0.98 mm in-plane and 0.5 mm to 7.5 mm through-plane. We utilize the full set of 361 publicly labeled instances.
OIMHS. A fundus retinal 3D OCT segmentation dataset comprises 125 sequences, each containing 19 73 scans, and is categorized into four classes: retina, choroid, macular hole, and macular edema. It is a anisotropic dataset, the spacing ranges from 10.7 um to 14.0 um in-plane and 7.0 um to 40.0 um through-plane. All 125 publicly available sequences are utilized in the experiments.
FeTA2021. A dataset consists of 120 T2-weighted fetal brain MRI reconstructions, categorized into seven classes: external cerebrospinal fluid, grey matter, white matter, ventriculus, cerebellum, deep grey matter, and brainstem. It a typical isotropic dataset, the spacing ranges from 0.43 mm to mm across all dimensions. All 80 publicly available sequences are utilized in the experiment.
AbdomenCT-IK. A large-scall scale abdominal CT dataset comprises 1,112 instances, focusing on the segmentation of four abdominal organs: liver, kidney, spleen, and pancreas. The spatial resolution ranges from 0.45 mm to 1.04 mm in-plane and 0.45 mm to 8 mm through-plane. anisotropic dataset, for our experiments, we utilize 361 instances from Task 2.
also an
BTCV. A dataset consists of 50 abdominal CT instances, categorized into 13 classes. It is an anisotropic dataset, with in-plane resolution ranging from 0.59 mm to 0.98 mm and through-plane resolution ranging from 2.5 mm to 5.0 mm. All 30 labeled instances are selected for the experiments.
4.2 Implementation details and evaluation metries
Implementation details. The datasets are randomly partitioned an 8:1:1 ratio for training, validation, and testing. To enhance segmentation performance, the data are resampled, intensity clipped, and normalized using min- max normalization. Specifically, the intensity clipping ranges are as follows: FLARE2021 [-125, 275). OIMHS [o, 300), FeTA [0, 1000), AbdomenCT-IK [-125, 300], and BTCV [- 175, 250). During the training stage, random cropping to 96 x 96 x 96 volumes is performed, and sliding window method with 0.5 overlap is used for validation.
All training utilizes the D function with the AdamW [55] optimizer, learning rate of 0.0001, 80,000 training iterations, and batch size of 2. Data augmentation techniques. including random flip, random rotation, random scaling, and random 3D elastic transformation, are applied to enhance dataset diversity and model generalization. Validation occurs every 1000 iterations during training to save the model weight with the best validation performance. The experiments are conducted on identical hardware and software environments, each workstation equipped with two NVIDIA

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Table I: Comparative experimental results of uC 3DU-Net and 9 previous methods the FLARE2021
and Fe 2eT2 datasets. The best values for each metric are bolded. Part of the data comes from [56].
Methods
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15.63
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56.5
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77.0
02.7
56.5
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Table 2: Comparative experimental results of uC 3DU-Net and 7 previous methods on the OIMHS dataset. The best values for each metric are high- lighted in bold. Part of the data comes from [25].
Table 3: Comparative experimental results of uC 3DU-Net and 6 previous methods on the AbdomenCT-IK dataset. The best values for each metric are highlighted in bold.
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GeForce RTX 4090 GPUs and 128GB of memory. The framework employs Python 3.9, PyTorch 2.0.0, and MONAI 0.9.0 within Distributed Evaluation metrics. We utilize IoU/mloU, Dice, ASSD, HD, HD95, VOE, and AdjRand as evaluation metrics to comprchensively assess segmentation performance. IoU and mIoU measure the overlap accuracy between predicted and ground truth regions, providing robustness across multiple classes Dice is particularly effective for medical images, especially in small target regions. ASSD calculates average surface distances, while HD and HD95 assess boundary accuracy, with HD95 focusing on the 95th percentile reduce outliers. VOE examines volumetric overlap and AdjRand evaluates clustering similarity, ensuring a thorough assessment of both overlap and boundary precision.
(DDP) training framework.
4.3 Comparison with state-of-the-art methods
Comparative experiments are conducted on proposed uC 3DU-Net and previous state-of-ther -hrt methods across five diverse, diverse.publicly available 3D medical image segmentation datasets: FLARE2021, OIMHS, FeTA2221, AbdomenCT-IKK and BTCV. The results are presented in Table 1, Table 2, Table 3, and Table 13 (provided as supplementary material in the Appendix). The FLARRE2021, FeTA20211 and OIMHS datasets are evaluated using five-fold cross-validations. The results from all five folds pass the Wilcoxon signed-rank test with a significance level of a=0.05, indicating statistical equivalence in data distributions. Additionally, the results from the AbdomenCT- K dataset all pass the one-tailed Wilcoxon signed-rank test with p<0.05, thereby validating the SOTA performance of the uC 3D U-Net. Overall, the result indicates that the proposed uC 3DU-Net achieved SOTA performance with fewer model parameters and reduced computational complexity compared to previous segmentation methods. On the FLARE2021 and FeTA datasets. uC 3DU-Net's 's parameter count and FLOPs are only 40.9% and 44.7% of the suboptimal model, 3D UX-Net, yet the average Dice scores improve by 0.7% and 0.4%, respectively, with a notable 3.1 %% increase in the pancreas category. On the OIMHS and AbdomenCT-IK datasets, uC 3DU-Net outperformed previous models by at least 0.6% and 0.99% in Dice scores, respectively, highlighting its superior overall segmentation performance. Additionally, uC 3DU-Net showed significant improvements in boundary performance metrics such as ASSD and HD95 on the OIMHS and AbdomenCT-IK datasets, demonstrating its enhanced ability to identify and correct boundary pixels by focusing on axial-slice plane features. These findings substantiate that the 2D Conv-based uC effectively compensates for the limitations of 3D convolutions in extracting in- plane slice information to enhance model performance. Notably, the performance improvement observed on the anisotropic datasets surpasses that on the isotropic FeTA dataset, further highlighting the efficiency of the proposed uC in enhancing in-slice plane information extraction, especially for anisotropic datasets enriched with axial-slice plane features. Overall, uC significantly improves in-plane feature extraction efficiency while maintaining robust axial spatial feature representation, representing the pinnacle of current performance and parameter efficiency.
4.4 Analysis
4.4.1 Evaluation of 2D vs. 3D convolution for slice-plane information extraction
7

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Image Reconstruction Fidelity Comparison. We evaluate three methods: 3D U-Net, 3D U-Net + 3D uC, and 3D U-Net 2D uC, on their ability to recon- struct axial-slice plane images from input 3D sequence images, with the results presented in Table 4. The high- est PSNR achieved by 3D U-Net + 2D uC confirms the proposed 2D uC's superior slice-plane feature extrac- tion and reconstruction abilities compared to 3D Conv.
Table 4: PSNR results of the reconstruction experiments on the OIMHS dataset. includ- ing three methods: 3D U-Net, 3D U-Net + 3D uC, and 3D U-Net 2D uC.
3D U-Ner(32)
+3D uC
+2D uC
PSNR
28, 98b
34.93db
36.29db
Impact of Convolution Dimen-
Table 5: Results of 3D U-Net, 3D U-Net + 3D uC, and 3D U-Net + 2D uC across different channel depth numbers on the OIMHS dataset. The best values for each metric are highlighted in bold.
sionality on uC Performance and Parameters. We conduct ex- periments on the OIMHS dataset
Methols
iParmns
FLOPss
mloU
ASSD
HDBS
AdjRand
using 3D U-Net, 3D U-Net + 3D uC, and 3D U-Net 2D uC across varying channel depths to evalu- ate the impact of 2D and 3D con- volutions on uC performance and parameter count. As shown in Ta-
(8)
83.1744.1
2.8822 16
16.92:21
29
+DD
5.661
46. 50G
95.5843.0
31
2.25a1
8.6421407
91.192240
UC
2854
42.54G
89.592261
0.5230.48
2.5764744
93.44116
(16)
1,4M
6568G
97.083343
09920001
4.5748922
20.2022:0
22.21M
186.27G
97.5023.34
93.082.11
2.
33.45219.56
12.41=215
11.38M
89.98+3.3
0.39p0.42
2.5664.9
94.066336
UD (3)))
488M
125.90G
97.08+3.32
02.79
1.31al
7.39
15
58.67M
596.06G
97.93+3.33
03.32-2.09
3.0Me133
14. 14.13216.99
45.33M
527.75G
90.52x2.77
94841118
0.35.0.33
2.20
94,431176
ble 5 and Fig. 5a (provided as supplementary material in the Appendix along with Fig. 5b), the 2D
ratio. This performance difference, independent of skip connections, is directly attributed to the differing feature extraction efficiencies of the 2D and 3D convolutions within the uC structure. This result is consistent with Table 4, further confirming the effectiveness of 2D convolutions for axial-slice plane feature extraction. Moreover, Fig. 5b indicates that the 2D uC converges faster and achieves a higher performance ceiling in the validation set, highlighting its greater performance in axial-slice plane feature extraction.
uC consistently achieves superior parameter-to-performee
4.4.2 Impact of uC on various 3D segmentation backbones
To assess uC's efficacy in over- coming axial-slice plane perfor- mance drop-off, we conducted quantitative analysis experiments on the FLARE2021 dataset using numerous state-of-the-art models. As depicted in Table 6 and Fig. 3, replacing traditional skip con- nections with uC can significantly enhance axial-slice feature extraction capabilities and thereby improve overall performance. Incor-
Table 6: Analysis experiments to evaluate the effectiveness and ro- bustness of the uC across different backbones on the FLAARE2021.
Mahool
4Paoms
FL.P%
mlol!
Dior
ASSD
HD95
AdjRond
SegiteNNe
188M
244 40
93, 9al 35
133111.29
65
982861137
SegResNet vac
20.5M
426.20
89.42a1.11
94,042106
0.808845
2.351100
93.42108
ThanBTS
3166
110.40
87.6322.74
92,84a1
10420664
3.542264
92.7341.81
TranRTS
36.M
94.0511322
0.786633
2514137
90054134
SwintNETE
622M
28.284700
91.234137
09940441
98134131
Swin UNETR +aC
754M
612
89.0742.00
93.7241.33
0.761841
2.511.14
93,62188
AD UI--Nei
53.0
699.40
88.6217
93.4341 98
2.504.00
1322746673
03 03.324100
3D UX-Net DuC
6066
86791G
89.622231
94.11a1:22
0.766833
2.551.31
910111155
porating uC exhibited i improvements in mloU and Dice scores by 0.79% and 0.65%-1.21 %,
respectively. Integrating uC into high-performing 3D medical image segmentation models such as 3D UX-Net, which initially achieved 93.43% Dice score, resulted in a 1.0% mloU and 0.77%
Dice score improvement. This demonstrates that even of-heoffthheart models encounter inherent axial-slice plane performance drop-off' issues, which uC effectively mitigates, thereby enhancing axial-slice plane feature extraction and overall performance.
4.4.3 Analysis on DFi
To validate the efficiency of the DFi module compared to the two 3x3 convolutions in uC 3DU-Net for integrating 2D and 3D fea- tures, we conduct analysis exper- iments on the OIMHS dataset to
Table 7: Further analysis experiments on DFI are conducted to validate its efficiency and effectiveness on the OIMHS dataset. The best values for each metric are highlighted in bold.
Matlnnk
4Ptrams
FL.PS
Dioc
ASSD
AdjRand
"CSsngel)
30.38M
450 870
90006-441
04 15-55-115
0.961185
703-19.11
04.14:3.11
98.422259
04.79.1.56
0.681119
94.38.1.2
30.28M
390.00G
19.75
04-37-1 92
0.3548.8
03.94-1.0 2.3644m
evaluate the impact of incorporat- ing DFi on the performance of uC 3DU-Net(32), considering differ- ent quantities and positions of uC module. As presented in Table 7, the results indicate that DFi achieves a slight performance im-
+Dii
23,49M
267.34G
98.033399
94.531193
0,490050
2.50e5
94.11+1.9
(Cbengee)
20
286
91
03.75-9112
0.4388442
2.70
03.2641. 18
+Di
23.
223.390
89.3363.30
94.132200
0,55e0.4
2.67
93,6863.3
uCisuge112.5) +Di
527.75G
90.52-2.77
91.842166
0.35a0:33
2.20
98.4341.6
38,53M
465.07G
75
9503e168
0.Mel445
2.3
provement with lower parameter and FLOPs counts than direct concatenation. This improvement
8

---

Original Image
GT
3D UX-Net
3D UX-Net +UC
Original Image
GT
SegresNet
Segres HMC
Figure 3: Qualitative results of the uC's impact on segmentation performance in 3D UX-Net and SegResNet backbones, applied to the FLARE2021 dataset. Segmentation results for different categories are represented in distinct colors. For improved visual clarity, the images have been cropped. Please kindly zoom in for better view.
Table 8: Analysis experiments to evaluate parameters and performance of 3D UX-Net across various channel depths and the integration of uC on FLARE2021 and OIMHS datasets.
Method
iParoms
FLOPs
mirli
Dice
FLAREZ001
ASSD
H095
Adjitand
mbeli
Dioe
OBMES ASSD
11T95
Adjitadd
6.7M
88,091336
93.12a1.99
1.05an61
89.0023.99
94.2812.00
2475495
93.842207
3D UK-NN (24)
13.4M
160.20
88 88.00
03.2441:6
1.632.11
7.4611.88
02.12.167
$7.8683155
03.27
0.64006:
368ab61
02.74,203
3D UK (24) Hat
15:0
23024
88.753309
93.85e1.00 107
0.92:0.99
2746144
95436133
90222233
M8.67e1.73
0.3999.4
2,3785m
94.25=1.79
3D UK Net (48)
530M
639.4G
58.022000
50.43e 19
2.5 2.50e5.00
13.2764673
01.52-000
25.35a3:25
93.5622.0 94
0:51:0:45
2.7084.5
93.054209
LXC-NN (488)
60.6
56791G
896221311
04.11A3:32
2.5361334
94.01113
90.3843477
0.3418.42
2.39.5000
94.3812:12
is consistently observed across different layers of uC, highlighting its robust and efficient feature fusion capabilities. The focus of the proposed DFi thus shifts towards optimizing feature fusion efficiency rather than merely achieving performance improvements. Continuous improvement efforts will prioritize refining 2D and 3D feature integration while maintaining computational efficiency.
4.4.4 Performance Comparison with Varying Channel Depths in 3D UX-Net
The low parameter performance ratio observed in 3D CNNs is largely due to their reliance axially symmetric 3D convolutions, which extract spatial features well but struggle to capture critical axial- slice plane details. Introducing the uC structure significantly enhances the capability of 3D CNNs to extract axial-slice plane features. Can incorporating uC allow a reduction in channel count, thereby decreasing computational cost? To explore this, we conduct analytical experiment utilizing 3D UX- Net as the backbone of FLARE2021 I and OIMHS datasets to investigate the impact of channel depth and uC integration on segmentation performance. As illustrated in Table 8, achieving 0.66% Dice improvement with the 3D UX-Net on FLARE2021 requires a tenfold model increase, highlighting the challenge of enhancing performance with 3D CNNs. The incorporation of uC mitigates the axial-slice plane performance drop-off, resulting in performance enhancements across 3D UX-Net with different channel depths. Notably, 24-channel depth uC 3D UX-Net surpasses the original 48-channel depth 3D UX-Net on both datasets, with parameter count and FLOPs reduced to only 29% and 34.3%, respectively. This highlights uC's capability to achieve superior performance with fewer parameters, suggesting that substituting traditional skip connections with uC and reducing channel depth is viable approach to achieve substantial reductions in parameter count and computational load while maintaining or even improving performance. The qualitative results are further illustrated in Fig. 4.
4.5 Ablation Studies
Stage Selection for uC integra- tion. By replacing skip connec- tions with uC at different stages, we validate the impact of intro- ducing 2D axial-slice plane fea-
Table 9: Ablation study of different stage selection for UC inte- gration the OIMHS dataset.
Methol
mloUI
Dice
ASSD
HDV5
AdjRand
37.0863.30
92. 79.2.9.
1.31ai:35
1.39
92.2242.15
+uC (stage 1)
90.06+3.11
94.55=2.16
0.96+185
7.95-19551
14.14+2.30
+oC (stage 2)
89.73x3.11 12
94.37a1.92
0.35+0.38
2.3654.6 69
93.941199
tures at varying network layers on model performance. All models had an initial channel depth of 32.
+oC (slage
88.71+3.31
93.75a2.12
0.456042
2.7045.1 17
93.2612.18
(stage
87.32-3.46
92.91-2227
0.69+0.33
3.0545.19
92.574231
+oC (stage 2, 3)
90.52+2.77
94,8421.68
0.35g0.33
2.2014.07
94,4311.76
+u (stage 2, 3)+ DFF
90.86.2.75
95.0331.65
0.34:1.45
2.3415233
94.63+1.73

---

Original Image
GT
30 UK-NET (16)
UX-NET (16) HUC
30 UX-NET (24)
30 UK-NET (24)+uC
30 UX-NET (48)
30 UX- EEX-NET (48) HUC
Figure 4: Qualitative results of the segmentation performance on 3D UX-Net and 3D UXNET+uC with various channel depths on the FLARE2021 and OIMHS datasets are presented. Segmentation results for different categories are represented in distinct colors. For improved visual clarity, the images have been cropped. Key regions the qualitative results have been locally magnified for better viewing.
As shown in Table 9, supplementing initial feature information in shallower layers, closer to the original image, resulted in better performance improvements, with deeper layers showing diminishing returns. Hence, balance parameter efficiency, we only replace skip connections stages Results indicate that using skip connections in the first three stages significantly enhances model performance, with the DFi module effectively integrating features extracted by 3D CNNs and uC.
Channel Depth Configuration. We experiment with channel depths of 16. 24, and 32 for the proposed uC 3DU-Net, all sur- passing the original 32-channel U-Net, as shown Table 10. Per- formance improved with increased channels, but efficiency dropped drastically at 32 channels compared to 24. Notably, the 16-channel uC 3DU-Net performed better than the 32-channel U-Net with similar parameter counts and FLOOPs, demonstrating 1.74% Dice score improvement and 4.95 HD95 reduction. This underscores that efficiently capturing slice plane features with uC significantly enhances computational efficiency in 3D CNN architecture. Thus, replacing skip connections with uC and reducing channel depth a straightforward strategy to achieve substantial parameter and computational load reductions while maintaining enhancing performance.
Table 10: Ablation study of different Channel depths of uC 3DU- Net on the OIMHS dataset.
Method
#Prams
FLOPs
mloU
Dize
ASSD
HD95
AdjRand
3D U-Nee
481M
135.9G
87.08-1
92. 70
1.31
7,39.um
02.22 15
MC 3DUL-Ner (16)
3.3M
134.3G
89.98-20
94 53.55. 6
244-50
94 94.11.. a
uC 3DU-Nat (24)
217M
286.40
90.674236
91.91::4
0.34
235531
94.53
DCC 3DU-Ner (32)
90866125
0.34
94.631m
5 Conclusion and future work
In this paper, we propose a U-shaped Connection (uC) for enhancing 3D CNN-based medical image segmentation architecture. This approach is specifically designed to address the inherent axial-slice plane performance drop-off in 3D CNNs, characterized by their inefficiency in extracting high-density axial-slice plane features, which are crucial for accurate 3D) medical image segmentation. By replacing traditional skip connections in 3D U-Net with uC. we further develop the uC 3DU-Net, capitalizing on more efficient feature extraction capabilities of both 3D sequential spatial features and 2D axial- slice plane features, reaching the best segmentation accuracy and computational efficiency ratio among all previous SoTA methods. Empirical evaluations on diverse datasets demonstrate that uC 3DU-Net consistently outperforms previous SoTA 3D medical segmentation methods, with notably reduced parameters and FLOPs. This underscores the transformative potential of the uC structure in revolutionizing medical volumetric segmentation by breaking through the intrinsic limitations convolutions. Future research will extend the application of the uC structure to broader range of volumetric segmentation tasks, with the aim of continually advancing the performance and efficiency of 3D image segmentation models.
10

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14

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Appendix
We first introduce the details of the datasets and implementation in Sec. A. followed by the supplementary experiments and analysis in Sec. B. Furthermore, the experimental results detailed in Sec. C. Lastly, additional qualitative results are provided in Sec.
A Details of Datasets and Implementation
A.1 Public datasets details
Here, we provide detailed information on the five public datasets used for experiments in Table 11.
Table 11: Detailed information of five publicly available Datasets: FLARE2021, OIMHS, FeTA02021, AbdomenCT-IKK and BTCV.
Datasets
FLARE 2021
OIMHS
Imaging Modality
Multi-Contrast CT Abdomen
OCT
Anatomical Region
Eye
Sample Size
125
Dimensions
512 (37 - 751)
512 x [19 79) {10.7 - 14.0) x {10.7 14.0).mmx
(0.61 0.98 mm {0.61 0.98 1.98)mmx
Resolution
(0.50 7.50
{7.0 40.0)um
Spleen, Kidney,
Retinal, Choroid,
Anatomical Label
Liver, Pancreas
Macular hole. Macular edema
Datasets
FeTA2021
AbdomenCT-IIK
Imaging Modality
1,5T 3T MRI Infant Brain 80
Multi-Contrast CT
Anatomical Region
Abdomen
Sample Size
361
Dimensions
256 x 256 x 256
(512 796] x {79- 512) x (31 1026)
(0.43 - me x {0.43 1.000mmxxx (0.43 1.00)mmm
(0.45 1.04
1m04)mm ma x (0.45 3.00
Resolution
(0.45 8.00
External cerebrospinal fluid, Grey matter,
Spleen, Kidney, Liver, Pancreas
Anatomical Label
White matter, Ventriculus, Cerebellum.
Deep grey matter, Brainstem
Datasets
BTCV
Imaging Modality
Multi-Contrast CT
Analomical Region
Abdomen
Sample Size
30
Dimensions
512 x (85 - 198)
(0.59 0.98 x (0.59 0.98
Resolution
{2.50 - 5.00).mm
Spleen, Kidney right, Kidney left, Gallbladder Esophagus, Liver, Stomach, Aorta
Anatomical Label
Inferior cava, portal vein and splenic vein, pancreas
adrenal gland right, adrenal gland left
A.2 Data Preprocessing & Implementation Detail
In the 4.2, provide detailed description of our data preprocessing process and offer more comprehensive hyperparameter settings in Table 12. For the BTCV dataset. we specifically incorporate resampling to specific pixel spacing(1.5, 1.5, 2.0) and random intensity shift as additional data augmentation strategies.
15

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Table 12: Detail hyperparameters of training scenarios five public datasets.
Training Steps
80000
Batch Size
2
AdamW.
1e-8
AdamW,
(0.9, 0.999)
Peak Learning Rale
le-4
Learning Rate Scheduler
ReduceLLOOmPlteau
Factor & Patience
0.9,
Flip, Rotation, Scaling
Data Augmentation
Intensity Shift, 3D elastic transformation
Cropped Foreground
Rotation Degree
(-30%, +30")
Scaling Factor
0.1
Intensity Offset
0.1
B
Supplementary experiments and analysis
B.1 Benchmarking results on BTCV
We present comparative experiments of the proposed uC 3D-UNet against previous classic segmentation methods on the BTCV dataset, as shown in Table 13. Given the smaller dataset size, CNN-based models outperformed Transformer- based models. The results indicate that uC 3D UNet maintains outstanding performance the challenging 13 classes BTCV segmentation dataset.
Table 13: Comparative experimental results uC 3DU-Net and previous methods on the BTCV Standard dataset. The best values for each metric highlighted in bold.
Methods
#Params
Fl.OPs
mIoU
Dice
ASSD
HD95
AdjRand
3D U-Net
4.8M
135.9G
72.25+1.30
82.24+2.99
1.06+0.40
4.22+1.35
02.19.3.00
TransBTS
31.6M
110.4G
67.8 07.85+3.55
78.38+3.50
2.01
8. 8.70+4.00
78.31=3.51
UNETR
92.8M
82.6G
68. 98..992252
79.82a2.51
1.3910.54
7.38u1.90
79.7612.51
UX-Net
53.0M
639.4G
72.2743.26
82.3123.06
1.37.0.45
4.70a1.49
82.25:3.06
uC 3DU-Net
21.7M
286.4G
72.99.3.00
82.74a3.18
0.97a0.21
3.6310.80
82.69a3.19
B.2 Additional comparative experiments
Here, we two recently proposed models as baselines for comparative experiments across four publicly available datasets: FLARE2021, OIMHS, FeTA2021, and AbdomenCT-IK. The experimental results in Table 14 demonstrate that our proposed uC 3D-UNet maintains superior performance compared to the latest models.
Table 14: Comparative experimental results of the proposed uC 3D-UNet against two added baselines on the FLARE2021, FeTA2021, OIMHS, and AbdomenCT-1K datasets.
Dalasets
#Params
FLOPs
Methods
mloU
Dice
ASSD
HD95
AdjRand
UNETR++ [30]
68. 59M
19.75G
83.98s4.45
89.9223.73
1.10a0.51
4.14
89.7943.75
FLARE2021
LKK Net [62]
34.64M
55.32G
88.80+2.58
93.56=1.88 1.82
0,71#0.28
2.53+1.05
93.46=1.83
aC Net-Net
217M
286.4G
89.36s2.26
93,98=1.46
0.6840.30
2.35+0.99
93.88+1.49
UNETR+ [30]
68.59M
19.75G
72.9042.00
1.1940.11
3.3740.85
83.56+1.75
PeTA2021
D-LKA Net [62]
34,64M
55.32G
73.35:3.83
84.28=2.64
1.19s0.16
3. 3.19:0.65
83.82:2.56
3DU-Net
21.7M
286.4G
78.6613.05
1.99
0.9110.09
2.3410.27
87.48:1.91
UNETR++ [30]
68.59M
19.75G
79.2747.49
87.0846.54
0,99+0.65
4.83+2.49
86.2646. 54
OIMHS
D-LKA Net [62]
34.64M
55.32G
86.55a4.11
92.39a2.85
0.44a0.27
2.28
91.8422.87
3DU-Nel
286.4G
90.67=2.95
0.3440.46
2.35+5.22
94.53
+ [30]
68.59M
19.75G
81.11:15
87.20=15.19
2.69:4884
9,70+12.43
87.04:15.24
AbdomenCT-IK
D-LKA Net [62]
34,64M
55.32G
87. 77.8443.33
93.08a2.13
1.4910.44
7.1619.08
92.9512.
3DU-Net
21.7M
286.4G
88.2954104
93.35a2.60
1.48=1.09
8.53210.74
93.22+2.66
B.3 Dimensional Slicing Analysis
To validate the effectiveness of the proposed uC in enhancing axial-slice plane feature extraction, conduct experiments with 3D U-Net 2D uC by slicing input tensors along different dimensions. As shown in Table 15. although the slicing augmentation along the time-axial slice plane dimension yields the best results. This result reflects the denser information available in the axial-slice plane of 3D medical images, which improves performance when using the proposed uC method. Furthermore, these findings support why 2D uC achieves superior parameter efficiency in 3D image segmentation tasks compared purely 3D convolutions.
consistently euhances performance across planes, slicing
16

---

Table 15: The results of applying 2D uC slices along the W, H. and D dimensions the OIMHS dataset. The best values for each metric are highlighted bold.
Method
miOU
Dice
Average ASSD
HD95
AdjRand
mIOU
Dice
ASSD
HDP55
AdjRaand
3D 32)
$7.08x3.32
22.2992
1.31x1.35
7.39e14.09
92.22x2.15 15
77 1..8966,77 74
$7.42s4.26
3.94 25
24.03=28.45
87 28.22
+2D uC(W)
88,01s3.12
93.35s1.96
0 0.8660.6
2,8945.53
92.84a2.05
$0.07:5.99
88, 88.8113.00
2.39
6.63=21.77
88 68
93.1662.46
+2D (C(D)
90.6742.95
94.9121.84
0.34:0.46
2.3568.22
94.53+1.89
83.57,5.21
90.9643133
0.80k1149
5.80g20664
90.86-3.17
Retial
Masika Elore
Charil
Dise
ASSD
HD45
AigRaed
n000
Bike
ASSD
HDM5
AijRaed
+dO0
Dive
ASSD
HDWS
AdBRae
12
01.75
96 Moens
0.16x0:11
1:144.00
66 13xt
$0.8544 16
60 to
1.05l 31
8M (iach)
ys 10.3744
on 90-i 11
B (7e in
7:34+ cn
us $1
<0.77:11
1:774:43
06.001
80:18-4
B.4 Visualization of the Impact of Convolution Dimensionality on uC Performance and Parameters
Here, we present visualization results from Sec. 4,4.1, as shown Fig. 5.
95
Noor
92
model
0.
3D U-Net
3D U Net + 3D uC
U-Net U-Net 30 uC
05
3D U-Net 2D uC
U-Met
90
8
16
32
10000 20000 30030
40000
Stap
50000 50000
70000
83000
Channel
showing Dice scores the OIMHS dataset, the channel depth for all three models(3D U-Net, 3D U-Net + uC, and U-Net
(b) Validation
(a) Performance comparison of U-Net, U-Net
3D uC, and 3D U-Net + uC across varying channel depths, with circle size representing parameter count. + 2D uC) set to 32. The horizontal axis represents the The horizontal axis represents channel depth numbers. number of training iterations.
Figure 5: Visualization of the impact 2D and 3D Conv on uC. (a) depicts the relationship between performance and channel depth. (b) shows the validation curve indicating the training efficiency.
C Details of experimental results
In this section, we present the details of our experimental results. For comparative experiments, as the basclines for FLARE2021 and FeTA2021 utilize data from [56], provide detailed experimental results for all categories in the OIMHS and AbdomenCT-IK datasets (with part of OIMHS comparative data comes from [25]. which are not included here), as shown Tables categories in the analytical experiments, while Tables 23 and 24 display the detailed data for all categorics in the ablation studies. The comprehensive results across all categories further demonstrate that integrating the uC module significantly enhances the backbone models' ability to extract 2D plane information from axial slices, while retaining the extensive volumetric feature extraction capabilities of 3D convolutions. This integration effectively addresses challenge anisotropic medical imaging, achieving superior performance with reduced computational complexity and fewer parameters compared to previous models. As a result, the proposed approach achieves an optimal parameter-te toneeteerttt--eeeee to-eerormaaccee ratio.
17. Tables 18, 19, 20, 21, 22 present detailed data for all
17

---

Table 16: Detailed results of uC 3DU-Net and 3 previous methods on the OIMHS dataset. The best values for each metric are highlighted in bold.
Method
iPazass
FLOP:
IdU
Dice
Avcraee VIDI
HIMS
AdjRand
IoU
Dice
Macslar Hole VOH
HD39
AdjRand
14P,33
240.2G
11.0087441
33.3
50056113
de:7d777 16
Tra
74
102.45417.55
UNETR
92
K1.44
38.1 1K.11s544
19, (M:30.3
30,07:5.68
57.3165.6
68.57e15577
17.12.00
47:00
15.03e
16.9912259
SDU-Nai
21.7M
286.43G
89.4561.66
94.13
10.52A5A1
2.988356
93.622266
80.8947.40
89,22
19.1
7.4177.00
89.0944.0
Hetral
Macatir Lidersa
Choosic
Diec
VOE
AiRuad
IJ
Dior
VOE
HDDS
Dioz
VOE
HDS5
AdRRaal
45 60
58 Slestar
15
$0.706
16:10447
25 #042-3
28 28:001330
3.541334
M7:24:214
04 10
31.2246676
4.18412550
90 202311399 10.09
01 0315a585
1238511932
10
92.961223
98377803
1J861.22
18.29...7
85.124871
91.556388
14.352133
8.254877
91.9WASS
91992337
98354137
6J862.11
2.183337
95.724379
Table 17: Detailed results of uc 3DU-Net and 6 previous methods on the AbdomenCT-IK IK dataset. The best values for each metric are highlighted in bold.
Method
Witrram
11.39s
IcU
Dice
ASSD
3ID95
AdRRand
ASSD
Liver
HDP55
AdjRand
3D U-N
4.51M
135.9G
85 09.0944.3
92. 02.2882.90
2.31a2.06
12.68a18.54 54
92.34:2.56
93.877a-0n On
96 96.55a2.21
3.57a3.66
22.45a28..0
96.53s2.35
ThasHTS
31 6M
110:403
700064.11 11
79. 79.644881 17.
688+1 -44
35.75410.m
74. 15-55-811 19
80. 11/79-6677 79
91-97-13 11-97-3..5
10.65-347
106-99-78.11
92: 72.70-1.14
UNETR
02.8M
82.66
84 174. 12
90.4245775
2.961: 63
43.41416.7
00:27-581
93. 81 81
96. 16..11
3:98-3:15
25.90 15.90-M 57
96: 06.17722:33
mifommer
149.5M
240.2G
90 30.0922.76
87.50 36
2.01 200
9:75ah
80. 07.38666.44 41
90.526681 51
96.05=2.70
1.1421.16
5.35c8.41
95 03.062.00
Swi UNUTR
62:2M
328.40 4G
56 7064.94
01. 18.33333 20
2.67::881
14.8741877
01. 00.2243336
94. 14.1555.3
96.95-2.06
4.11a2.83 83
21 21.27:55664 64
96 6442..55
UX
53.0M
5664
02.21as-18 18
15.46119775
02.07
97.05u1.01
3.8483.57
27.57 39
96.75a2.19
3DU Net
21.7M
286.46
06.29:4044
M3.35a2.00
1.45elmm
8.5368874
93.33a2.66
95.0753.5
97,461.30
1,71e1.70
7.922199.0
97.1863.17
Soleer
Sdaas
Fastza
18745
Atfaar
1:41
Dioe
1:124166
4:754706
30
10
35
1310033.3
24384487
90 115451
55 15
57.05:19.0
93 $3
94 34
1
(L.563-0 4)
01377446
35
8750100
1354148
38
5015-1335
3 13542 56
17 12
13.461177
15
35 13663333
1.30.:00
508660.77
16. 05.1142.0
96.0-1100
12
310
2014:65
9.38611255
99 31.1244.2
5155144
35 25.50u2:2
0.5518m:
1,90422
05
05.402236
90.53AAM
25.07443999
55 05 49
08.07.1100
77448 96
534 12
9) 20.29::22
92.334437
35.8922.4
0.554187
43843393
94.16477
96.222499
1386335
3833823337
98.262232
72.7229-2
85.5467.11
1.398..5
4.753377
83.853335
Table 18: The detailed results of 3D U-Net, 3D U-Net + 3D uC, and 3D U-Net + 2D uC across different channel depth numbers on the OIMHS dataset. The best values for each metric are highlighted in bold.
Method
#Paams
FLOFs
mloU
Dice
Asarass
HDMS
AdjKand
mloU
Dicee
Muscalar ASSD
AdjRand
(:Nooi))
0.37M
15,900
83.1714
00:00-3:0
2.88
16.02.31
30.51
71.2246
$2.01.5.4
9.48
00:45
62.73a5.81
43D
5.55M
46.80G
55.
91,36c2.35
2.25a1 35
5.6414407
91 11.1922 40
74,08s5 84
54.5m3.86
5,
27.70
54.79e3. 86
288
42.54G
89.59x2.1
94.321.62
0.52e0.00 08
2.576474
933461165
65
82.096474
90.99:3.91
1.13
5.99
89.9752.4
3D Nei(16)
1.4MM
65.09G
87.0553343
92.77=2.24
0.9229
4.5728.92
92 2022222200 20
79. 15.55 10
59 23:33.
1.57
6,54219 25
58.11e23.7
+3D MC
22.21M
185
87.501334 34
93.00e2211 11
4.5kel 25
43 43.45229.56
92. 92.41=2.15
79 19.33ak 99
1.23el 93
7.30 24
59 58.2944453
22D UC
11.5M
169 200
50.981345
25
0.39
2.5614.96
94.0611.16
36
$1.6947.32
0.801137
6.111199755
59.6114344
3D Ne113))
488M
135. 90G
87.0543.32 32
92,70:2.00
1.31e1 35
7.2921440
92
77, 19.89 74
47.422
3.942525
24.0025416
57 $7.28c4330
88.67M
596.60
87.934155
01.32:2.0
31
1413.116
79.63.6 31
96.44 19
18 3944.4
45.3M
527.750
90.221277
94.461.8
0.35
2.201487
94,33176
82,601833
90.40.344
6.7611166
5.11A86.15
90.201331
Bertell
Mandari
Cheend
Disc
HD55
HD55
Mijual
HDSS
AijKasl
27-2511-71
98 Muunne
122000
1414J54
2231139
13034140
134154
5774039
33352444
972861.00
01558312
112.0.0
18.28411
14
91446432
0.26833
1222155
96336099
1.4485
0132214
Li5uns
103,134
5105-333
05
2022144
3.011335
13::155
02.5:214
06.946776
V8 18
92.13-577
145419
97 20240
1,57x133
W633-136
97 96961 14
91
0216114
66 201130
93 2b3311
50.58:5.8 89
50.66539
9101-224
371.331
51122%
33 30
03 24
37.351137
85.880
90.29-334
0222832
140
00.15:34
07.00006
0.20
1.55a135
96
Table 19: The detailed results of the analysis experiments evaluating the effectiveness and robustness of the uC across different backbones on the FLARE2021 dataset.
Method
#Purams
FLOPS
loU
Dice
Avcruge ASSD
HDBS
AdRaadd
IoU
Dice
Liver ASSD
HDRS
AdjRand
SeaBeaNNrr
18.3M
20.3021.15
1.3121
6.77m144
03.2841.2
20er159
0.55am33
14489444
38.00es01
Neg
31AM
92.3c11 56
1,0650
11.7341
50 56.996057
0:52:026 26
1.546249
97 65
Ti TransBTS suc
36.6
176 40
89.4622.13
94.551.33
0,788m330
25111117
93.9581.4
96.551122
98.1998184
0.50em
1.5388644
97.9888173
Swis LINTTR
62.2M
338 40
88.2813.M
03.33:1143
0.05a141
3.25x3.15
03.1331 86
95-4211
98. 17111M
0.53an
1661246
07.0711144
Swis UNETR vot
75.0M
612 36
8907722.0
93.72e1.3
0.769441
2.5641.44
93.624185
96.984117
98
26.6660060
0.50e4
1.5440.51
98.0550733
53,M
28.0242.00
93.45=1.16
03.3242.0
9.7622347
0.760135
15311.34
98.3430044
0.460155
98.14
Paxtza
Tioc
11095
MjRoad
Dice
AS8D
1099
AdRivd
10095
MiRind
45
2.263300 00
32564 55
11912439
15,27231 62
11152100
1.5722-12
95.790125
90.33000
0485033
07.398044
06 T08300
29 754591
20020 54
1.522855
033431333 34
96.3611 50
11111083
VA 1LSSA120
1.24005
144U871
57723348
10-8-635
M4131M
2310367
1.338619
21
11661 15
5:273334
$1.46629
1681a2
B/22844
1.272359
3141803
00 408000
30 88.6.77
4.9812211
205445159
16.91-1344
14
57
13400
1.33
K-3541.1
$2.98-3318
18

---

Table 20: The detailed results of the analysis experiments DFI are presented to demonstrate its efficiency and effectiveness on the OIMHS dataset. The best values for each metric are highlighted in bold.
Method
WPrrams
F1 11.0%s
Disc
Asstnge ASSD
HD95
AdjRand
Diax
Macular Hole ASSD
HD95
AdjRand
uCIssage))
30.38M
439.87G
90.00:334 Al
94.55e2.16
0.96al .85
7.92219.51
94.1422.20
82-49P6.14
90.26:345
0.72e1.8
5,8922022
98.1723.88
+Di1
23.58M
377.19G
90.4222.99 29
94.7921.56
0,08
4,45+11/33
94.3-1433
82.34
90.25e3008
2.0664.71
14.18644:01
90, 90,12+3305
UCtsugec)
30.25M
350.01G
89.7383.12
91.37a1191
0.35
2.M6.4.09
93.9121399
82.4765.0
9002963346
0.69a1.34
4.57
+D81
23.49M
207.34G
90.07=3009
94.53e1.33
0.09e0330
2.50p5.19 19
94.11=1.99
N2.7665711
90.4563.54
1.50e).33
0.00-20:33
10.343 56
03.7542.13
0.434843
1.70-117
0%%
5.84-31.44
00. 70-166
+D61
23.12M
89.3343
94.1363.07
0.556066
2.0755300
93.68e1.33
82.1966.36
91UPP4403
1.41:247
6,27:21
89.974404
KChungel 2.9)
45.35M
527.
750
50.5042.77
96.84:1.54
0.55+0:31
2.20-4.7
96.41-1776
62,664567
90.40-3.45
162661.0
5,11-16.15
+1261
18.53
465 070
98.864275
95.1541.448
0.3448.45
2.36-325
9463-1775
R3.645.31
91.836834
0.704146
573--000
98.9241.26
miOU
Dust
ASSD
HDMS
AddBaa
Dic
ASSD
HDM5
AdjRas
Dies
AS5D
HD95
Adiand
6:142811
51
1.162234
30
1122043
25
96.44604
92.44439
02316635
96.322122
97.00:344
0.M
96
97.8901
015-9
93-9-511
1.194477
11
91.061 2
1.374015
97.701125
98 Saaus
0.170:14
LiSansa
51.3eaus
29 21
0.368.11
137
20.32ax22
01
969221
0455033
14
05 05
97.891133
9691
6.358813
96.66133
K2.764700
90.44.33
03566335
121.841
M1326510
94.341.78
97.08433
1.384.23
20541.6
77
0.13-8112
77
47 78
0.75-333
107-351
45.461..0
97.661111
0.246.11
96.94433
91.04477
016-411
1.33444
02.54-144
8.23-833
02.464866
45 7741
53
07 17455555
03306
1.514000
Table 21: The detailed results of the analysis experiments evaluating parameters and performance of 3D UX-Net across various channel depths and the integration of uC on the FLARE2021 dataset. The best values for each metric are highlighted in bold.
Mathal
APaxa
FLOP
1oU
Dise
ASSD
HD95
A.BRaad
IoU
Dice
ASSD
Lisrr
HD95
AdRand
3D UXC Net (16)
6.15
72.2G
67.662340
92.77a2.55
1.50a2166
9.76117
92.66s2.33
95.06:1-49
97 07.98a0.75
1.5364000
10.143399
97:75au
UKK-Ne (18) =ox"
67M
91.16
880996336
95.1241.89
1.854861
2.991.26
9X81-141
96.4-11.4
98.176800
6,81-6234
1.88
97.9666676
13:MM
100.0
24e1
1.692.11
7.4el1
09.1+2.0
2.576719
13.5164347
93,4361.5
58.000135
3D UX Net (48)
53.0M
639.4G
58626200
95.43s1.36 56
2.59e5.00
13 13.27 13
53. 53.522200
96.
98 00.2063
1.492233
9.76235.47
95 19
30 UX--Na (48) HGC
60AM
W1621311
94.1841.55
0.7648255
2.5341.34
94.01.155
96733.124
18.3448M4
0.468225
1.4
9K.14
Kitery
Splon
Paexm
Bioc
ANS0
10045
AjRevl
Will
ASSD
1095
Doee
A580
1009
ArRRwl
RAADAZAT
1235045:50
16.15-23
1:561300
07 14.56
06 1t
2334133
92 8755551
2632254
192118
96
97 90uenn
1.30833
97 7700000
48.1
26433
651:19)
4100
W7
13-13-333
73
47 67
07 5741 09
58 10w 9
154.71
231417
615-10
51 (1401
331845111
N0.3723 55
8,07000
2555245
96232172
96455144
97 9759887777
0811144
1163835
91058079
01 2359111
1484859
588240
81 8158072
57.77mm
94156330
16. 56
3.292252
35.36133
98 35.15
135563
18.10607
71
5:55441
K2986529
Table 22: The detailed results of the analysis experiments evaluating parameters and performance of 3D UX-Net across various channel depths and the integration of uC on the OIMHS dataset. The best values for each metric are highlighted in bold.
Mathad
Whrmm
FLOPs
16U
Dice
ASSD
HD95
AdfRund
Dise
Maculer ASSD
HDD95
AdjRand
6.1M
72.30
86 36:66
92.1 02.154311
19
1:12.000
5.70u3000
91 5441.2
77.3144.53
86-034124
2.80uS
16.77413.1
86, 78 TD
3D UX (16 sUC
6.7M
97.3G
W9
94.2862.00
0.3651.40
2.475495
93
N1.4N66-53
W966448
0.7661.55
5.9711934
89.545410 10
(16)
13:4M
100.20
57.863315
93.2722105
3.68
02
79.5825:0
59.5133.1
1.2221.9
85.392233
UX Net (40)
530M
639 4G
88 15-35333
01
0.1-0.15
2.70 86
91105-200
1266.9
1,07-1 74
00419
3D UX (48) vuc
60.6
967910
90.382447
94.732155
0.342043
2.3915.00
94.336222
82.20
90.1023.06
0.74
5.3331975
89 89.992319
Reiis
Edeera
Cheooii
Tioe
A35h
1895
AgRaad
100
Diee
ASSD
AggRasd
Tice
ASSD
11195
AigRaed
96324677
8.16.4.13
182161.55
91.166322
8.2866.15
1.844657
91996525
08000100
97.45685
8:3548:17
1.09411
2.
97 T341
6.20- 16
(15615)
1061
6.95+1 60
475745554
19 18x5is
10
95
07. $151.33
96, BFaroo
0.16c9 1)
06 Ka1.00
92 0967-99
59 54555
4.34
1.21at00
59 5151ss
03 4062353
36.771111
0:478825
2.431:55
05 03.331137
98 (651..5
9983367777
8.14:813
11869.4
98.
85
35.9789336
92:1365.30
6.285433
1,873600
92 135325
95.3711.56
97.578002
8.298815
1.478881
96 96.381
19

---

Table 23: Detailed results of the ablation study different stage selections for uC integration in 3D U-Net. The best values for each metric are highlighted in bold.
Avverage ASSD
AdjRand
IoU
AdjRand
08e3
1.31=1:35
7.39m14.0
8988
57.28e4430
+uC (stage
0.9Kall.5
7.95
90.28-3.85
0.7241188
5,80420822
10.17e3388
+0C (stage
77355-12
94, 37
0.35a0.16 16
2.368469
03.0481.04
474477558
90:29:3.48
0.6983.54
5.57a18
+0 (Mage
75
0.45e0442
17
80-91-345
0.86+161
+uC Istgg 5)
87.3223.46 46
92. 22.93
0.6929063
3,08
92.7742.31
78. 0115511
57,9354.01
1.3821192
6.65220.50 58
87.80144.01
+o.. (stage 1.2,
50..5 21.52222777
94 8441..5
0.35xa0:3
2.20e54.17
94.454 94.48e1.76
82 02.6645.6 63
90.40tt:148
0.76m in
5.11116..15
50. 90.29t11.11
HuC (stage 2,
D3FT
90.8622.75
95.01311.48
0.3428845
2:34 23
94.0331.733
K3.0M55.31
91.03:3344
0.70ts .86
5,75e20 mr
90.92x3.26
Ronnal
Mactiar tutersa
Citendd
Dice
ASSD
HDBS
AijRaad
1:U
Bike
ASSD
HDDS
AdRaad
Bike
ASSD
HDWS
AdBRue
1.52144
08.0041.5 St
Mald 74
0.344015
Lib48.13
96. 16.15413 11
B6 T146 10
01:51-5 14
11
115-076
91 m0s 15
MC 9009
20.974700
1 864641 75
27-8141/0
x1
1:154114
115-84
1040.0
00:1-511
114-0:0
1.534077
27. 21.201153
38.3322.30
0,772115
06. 06.07a1..22
50.4285999
18.9525.10
0.4921.3
14721.4
88 83.718330
56.0811.2
0.668333
20452.36
04 04.38:220
8.334613
1.124443
187-441
18-134
07.6846011
Table 24: Detailed results of the ablation study different channel depth configurations of uC 3DU-Net on the OIMHS dataset. The best values for each metric are highlighted in bold.
Method
Dooz
ASSID
HT355
Adjiand
Dior
ASSD
HDDS
AdjiRand
3D U-Nes
4.3M
135.9G
$7.08 0885 12
92.
79a2200
1,3
7.3821410
92 92.22a2.15
77. 77.8926-74
87.42e4-25
3,94255 25
24.30458.55
S7. 23.2322433
IC: 1XUL-Ne (16)
1M
134 913
59 986-249
2.6445000 o6
+1 A3
82 27.88-55 18
90 20 36
10470 11
Un 11441
MC: 3D0 Net (24)
21.7M
256.4G
90.0 90.672295
94
0.342346
2.3825:22
94.5311 20
83.5745:21
0,8021 19
5,80220644
90.8023117
MC 3D0
Net (32)
38.5M
205.14
90.662775
950331
0.348445
2. 3.3455-33
91A33L77
830865.31
91.0055334
0,70g1 66
5.73630669
98,9263.66
Kental
Morntar Eaweaa
Chones
AiRaai
160
ASDD
HD15
Aijass
Dice
ASSD
AijRand
96.22119
1:388511
1.22144
18-155331
W.DIAE
IL3980.23
205a1.97
14.3341:22
VERSAL 56
11:48013
1.550011
18-23AL3:
112189177
91.346425
114480314
1.415117
16.3281.5
99991111 11
18611
0.258833
98
99.069978
6.3406222
1.138643
$6,351.99
92.566111
0.326923
1.00H177
92.446295
1:566030
96 66.22611
D Qualitative Results
In this section, present additional qualitative results. Fig. 6, Fig. 7, Fig. and Fig. further show the superior segmentation accuracy the proposed uC 3D U-Net compared previous models on the FLARE2021. FeTA2021, OIMHS, and AbdomenCT-Ik I- datasets. Fig. 10 visualizes the impact of integrating the uC module with backbone models the FLARE2021 dataset. These visualizations demonstrate that the integration of uC effectively enhances the model's ability extract axial-slice planc information while retaining effective volumetric feature extraction, addressing challenges related anisotropic medical imaging. This integration achieves improved performance with reduced computational cost and fewer parameters, resulting in an optimal parameter-to-performmee ratio compared to previous models.
Original Imsoo
uc 3DU-Net
Figure 6: More visual results of the proposed uC 3DU-Net on the FLARRE2021 dataset. For enhanced visual clarity, the displayed images have been cropped. Please kindly zoom in for better view.
20

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Original Image
GT
UC 3DU-Net
Figure 7: More visual results of the proposed uC 3DU-Net the FeTA2021 dataset. For enhanced visual clarity, the displayed images have been cropped. Please kindly zoom in for a better view.
Original Image
GT
nnFormer
TransBTS
UNETR
uC 3DU-Net
Figure 8: The visual comparison of the validation results on the OIMHS dataset for uC 3DU-Net, and previous segmentation methods. We have selected representative sequences for display. For enhanced visual clarity, the displayed images have been cropped. Please zoom in for better view.
21

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Original Image
3D U-Net
nnFormer
Swin UNE TR
TransBTS
UNETR
UX-Net
3DU-Net
Figure 9: The visual comparison of the validation results the ABCTIK dataset for uC 3DU-Net, and 6 previous segmentation methods. We have selected representative sequences for display. For enhanced visual clarity, the displayed images have been cropped. Please zoom in for better view.
Original Image
GT
3D UX UX-Net
3D UK UC-NetiuC
Original Image
GT
Sagres Not
Netsuc
Original Image
UNETR
UNETR
Original Image
TransBTS
Trans
Figure 10: Visual results showcasing the differences integrating uC for 3D UX-Net, SegResNet, SwinUNETR, and TransBTS. For each model, We have selected two representative sequences for display. For enhanced visual clarity, the displayed images have been cropped. Please zoom in for better view.
22

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Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The primary claims focus the integration of 2D U-Net-derived skip connections into 3D CNN architectures, specifically addressing the inefficiencies in volumetric feature extraction due varying information densities along different axes, presented as uC and enhanced uC 3DU-Net, is rigorously validated through various experiments on multiple datascts, improving performance with reduced computational complexity.
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14. Crowdsourcing and Research with Human Subjects
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28


Reviews:
[{"review_id": "G2XT7OoUs8", "reviewer": "Reviewer_Lrjd", "summary": "This paper proposes an approach to combine 3D and 2D features for medical image segmentation. The key observation is that medical images often have high in-plane resolution (2D) and lower through-plane resolution (across 2D slices). They introduce a U-shaped connection which uses 2D convolutions in place of standard skip connections in the widely used UNet architecture. They demonstrate segmentation results on four public datasets across a variety of imaging modalities and organs. Their method is computationally more efficient as there are fewer parameters needed by pushing some computations to 2D.", "questions": "- How are the images reshaped to be run in the 2D UNet model? Is the 3D image tiled to create a 2D image?\n- How would network performance change for a high resolution, isotropic dataset? For a dataset with low out-of-plane resolution?\n- Why does the nnUNet model have substantially more parameters than your model?", "limitations": "Minimal limitations are mentioned.", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "This paper presents a simple and clever modification to existing UNet-based networks for medical image segmentation. The key idea is to add 2D UNet feature extractors to several layers of a 3D UNet to better learn the high resolution in-plane features. They augment these features with the typical 3D features in a DFI block. The idea is simple and flexible, and can be incorporated into existing UNet-based architectures.\n\nThey evaluate their proposed approach on four public datasets and compare with 9 baseline architectures. They also include several ablation studies testing their method. They achieve improved results by1-2 Dice points compared to baseline approaches.", "weaknesses": "The method is a fairly simple architectural tweak that improves performance very slightly, on already well-studied segmentation tasks. Consequently, there should be substantially more experimental evaluation to properly conclude the benefit of this approach over other methods, especially attention-based approaches. \n\nThe main claim of this work is that using an isotropic 3D convolution will ignore or will not accurately learn in-slice features. Additional experiments should have been done to demonstrate this. For example, how would this model perform on a highly nonistropic dataset, or a high-resolution, isotropic one (such as brain MRI)?\n\nAre the table results shown on the test set or the validation set? How was the validation set used?\n\nFor the baseline models, I believe additional details should have been provided:\n\n- how was the hyperparameter tuning done? Was there a proper grid search to determine model depth, regularization parameters, etc...? For example, the very poor performance of nnFormer in Table 10 and of TransBTS in Table 11 are concerning.\n- The set of data augmentations is quite limited, and may have hurt the models with a larger number of parameters (e.g. transformer-based). Additional nonlinear augmentations should have been performed. \n\nThe ablation studies also reveal negligible improvement when adding the uC to an existing model (Table 5). This is perhaps the most revealing experiment of this work. \n\nAll experiments should have associated error bars. The differences are far too small to make any meaningful conclusions. \n\nI am also not convinced that the main claim of the paper is proven:\n*While 3D convolutions capture sequential spatial feature information, their efficiency in extracting 2D plane information is inferior to that of 2D convolutions applied to individual slice images.* (lines 169-170)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the thoughtful review of our work! Below are the responses to your concerns. \n>W1: More experimental evaluation.\n\nWe conducted experiments involving two attention-based segmentation models, D-LKA-former[1] and UNETR++[2], across four datasets, with results presented in pdf.Tab.1. Updated manuscript will include the five-fold cross-validation results for these newly added baselines.\n>W2&Q2: How would this model perform on a highly nonistropic dataset, or a high-resolution, isotropic one?\n\nComparative experiments were conducted on the anisotropic BTCV standard dataset (in-plane resolution:0.54*0.54-0.98*0.98mm², slice thickness:2.5-5.0mm). The results, shown in pdf.Tab.5, indicate that our method performs favorably on this anisotropic dataset. Additionally, within our datasets, FeTA represents a typical isotropic brain MRI dataset (spatial resolution:0.43-1mm cross all dimensions), while OIMHS is a highly anisotropic dataset (spacing:10.7-14.0 µm in-plane and 7.0-40.0 µm through-plane). The performance improvement of uC on the OIMHS depicted in Tab.1 and 2, exceeds that on the FeTA, underscoring the efficiency of the proposed method in enhancing in-slice plane information extraction, particularly on anisotropic datasets.\n>W3: ...shown on the test set or the validation set? How was the validation set used?\n\n​All results are conducted on test sets, with data partitioned in an 8:1:1 ratio for training, validation, and testing set. Validation occurred every 1000 iter during training, saving the model weights with the best validation performance. We will include all details in the future version.\n>W4: More details for the baseline models.\n\nWe adhered to validated hyperparameter settings in previous papers for baseline models, avoiding additional grid searches for hyperparameters like model depth and regularization. The training protocols and epochs for models like nnFormer and TransBTS matched those from established papers, with code available for reproducibility. Data preprocessing for comparative experiments was consistent with methods from the 3D UX-Net[3] paper. For transformer-based models with large parameter counts, training involved extensive data augmentation techniques on over 300 cases from specific datasets.\n>W5: Experiment about adding the uC to an existing model.\n\nExperiments of uC in pdf.Tab.5 highlight two key insights. First, in fully optimized 3D segmentation tasks, an Dice improvement of 0.43% to 1% is significant, as shown by the FLARE2021, where a 1% gain necessitates a tenfold model increase, indicating the challenge of enhancing performance with convolutional architectures. As pdf.Tab.2, transitioning from 3D U-Net to 3D UX-Net[3] in OIMHS yields only a 0.85% Dice improvement, yet this still validates the efficacy of 3D UX-Net, as recognized in the field. The observed Dice improvements of 0.4%-1% across models such as 3D UX-Net and Swin UNETR[4] confirm this advancement as substantial in 3D segmentation.\n\nFurthermore, the analysis in pdf.Tab.5 demonstrates the method’s cost-effectiveness compared to simply increasing channels. The 3D UX-Net, with 24 initial channels and the uC module, outperforms the 48-channel version while reducing parameters and FLOPs to under 30%, emphasizing the method’s contribution to lightweighting. This efficiency, with 15.4M parameters surpassing a model with 340% more, underscores the method's effectiveness.\n>W6: All experiments should have associated error bars.\n\nThe updated manuscript will include standard deviations for all experimental results wherever possible, similar to those provided in pdf.Tab.1.\n>W7: Main claim of the paper...\n\nTo address the concern regarding the efficiency of 2D convs in extracting slice-plane information, we have conducted additional experiments:\n* Convolutional Dimensionality Performance Comparison: Visualization results in pdf.Fig.2 demonstrate the relationship between dice scores and parameter counts for three models: 3D U-Net, +3D uC, +2D uC. The performance differences between the 2D and 3D convs in the uC, excluding the impact of skip connections, indicate the superior efficiency of 2D convs in slice-plane feature extraction.\n* Image Reconstruction Fidelity Comparison: We evaluated three networks—3D U-Net, +3D uC, +2D uC—on their ability to reconstruct input images, with the results presented in pdf.Tab.4. The highest PSNR achieved by 3D U-Net+2D uC confirms the proposed 2D uC structure's advantage in extracting slice-plane features.\n* Dimensional Slicing Analysis: Experiments slicing input images along different dimensions reveal that slicing along the plane dimension yields the best results of uC, as shown in pdf.Tab.2. This reflects the denser information present in the Depth dimension of 3D medical images, enhancing performance with the proposed uC approach. These additional experiments collectively verify the enhanced efficiency of 2D convs in extracting in-slice features compared to 3D convs.\n\n**Q1:** Images are reshaped by stacking slices along the batch dimension, implemented in code with 'einops.rearrange(x, 'B C D H W -> (B W) C D H')'.\n\n**Q3:** Parameter comparisons including nnUNet, follow the data reported in the 3D UX-Net[3]. The smaller parameter count for our model attributed to fewer channels and the use of DFi. The pdf.Tab.6 provides detailed parameters comparisons when using DFi.\n\n[1] Azad, Reza, et al. \"Beyond self-attention: Deformable large kernel attention for medical image segmentation.\" WACV. 2024.\n\n[2] Shaker, Abdelrahman M., et al. \"UNETR++: delving into efficient and accurate 3D medical image segmentation.\" IEEE TMI, 2024.\n\n[3] Lee, Ho Hin, et al. \"3d ux-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation.\" arXiv, 2022.\n\n[4] Hatamizadeh, Ali, et al. \"Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.\" MICCAI workshop, 2021.\n\nThank you once again for your valuable insights! (＾ω＾)", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nxPhITcZBe", "reviewer": "Reviewer_WWao", "summary": "The study introduces uC 3DU-Net, a 3D medical image segmentation method combining 2D U-Net skip connections with 3D CNNs.", "questions": "Please visit Weaknesses.", "limitations": "Please visit Weaknesses.", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Experiments were conducted across multiple datasets with several relevant baselines.\n- Conducted comprehensive ablation studies and model hyperparameters are reported.", "weaknesses": "**Motivation:** The justification provided for the selection of a 2D UNet over 3D convolutions in Section 3.2 is somewhat insufficient. The authors argue that 3D convolutions are less efficient at extracting 2D plane information; however, this assertion does not consider that using 2D convolutions on 3D medical images can compromise the spatial relationships between slices, thereby limiting the capture of intra-slice features. Could the authors elaborate on their rationale behind this choice? Furthermore, the novelty of the proposed approach appears primarily limited to the incorporation of a 2D UNet as a skip connection. A substantial portion of the paper (approximately four pages) is devoted to discussing previous methodologies, while the method is described only in Section 3.3 and this makes it hard to see the contributions of this work among the extensive discussion of other research. \n\nAdditionally, the reference to 3D convolutions in Line 195 seems inconsistent with the dedicated use of a 2D UNet and requires further clarification. The main figure (Figure 2) also lacks a detailed representation of the blocks used in the 3D UNet, which obscures understanding of the architecture.\n\n**Experiments:** To robustly support the choice of a 2D UNet, a comparative analysis with at least one 3D UNet or 3D convolutional model would be highly informative. This would provide a stronger justification for the choice of model architecture.  Furthermore, testing the models on more challenging datasets, such as the BTCV 13 class dataset, would better demonstrate their efficacy and robustness. There is also a discrepancy between the number of parameters listed for SegResNet in Table 1 and Table 4 that needs to be addressed for clarity and accuracy in the reported results. \n\nSince the addition of the DFi module results in only minor improvements, the performance gains claimed could be attributed to random variation, given the very small difference in the provided metrics. This minimal change does not convincingly demonstrate the effective integration of 2D and 3D features.\n\n**Minor comments:** \nFix the AdamW citation in line 246.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your careful review of our work! We would like to take this chance to address your concerns and respond to your questions.\nW-Motivation:\n>W1: The justification provided for...\n\nThe utilization of 2D convolutions potentially undermines spatial relationships between slices in 3D imaging; thus, comprehensive experiments are imperative to ascertain both the enhancement of planar performance and the impact on 3D spatial relationships. Extensive experiments unequivocally demonstrate a significant improvement in the performance of 2D convolutions with negligible detriment to spatial integrity. CT scans produce independent images through multi-angle X-ray scanning, whereas MRI relies on magnetic fields and RF pulses to excite hydrogen atoms, imaging only at specific resonant slices. Therefore, the spatial disruption by 2D convolutions in 3D sequences is minimal, whereas processing each slice independently is crucial. This hypothesis is substantiated by extensive experimentation:\n\n* Image Reconstruction Fidelity Comparison: Three networks—3D U-Net, 3D U-Net with 3D uC, and 3D U-Net with 2D uC—were employed to reconstruct input images. Evaluation of the reconstructed planar images, as shown in pdf.Tab.4, revealed that the 3D U-Net with 2D uC achieved the highest PSNR, indicating superior feature extraction and reconstruction capabilities compared to 3D convolutions.\n\n* Convolutional Dimensionality Performance Comparison: The relationship between the dice coefficient and parameter quantity for the three networks under different initial channel counts was assessed (pdf.Fig.2). The performance disparity directly attributed to the 2D and 3D convolution efficiency in the uC structure was evident, excluding the influence of skip connections. As shown in pdf.Tab.3 and pdf.Fig.1, the 2D uC network converged more rapidly on the validation set with fewer parameters, demonstrating the superior efficiency of 2D convolutions in planar feature extraction.\n\n* Dimensional Slicing Analysis: Comparison of slicing along three dimensions in the uC structure (pdf.Tab.2) showed that, although the information density varies across dimensions in 3D medical imaging, enhancing 2D information in any plane slightly improves overall performance with minimal spatial relationship disruption. This justifies the extraction of 2D features within 3D segmentation models under the premise of capturing inter-dimensional relationships via 3D convolutions.\n\n>W2: Furthermore, the novelty...\n\nThe research primarily addresses the challenges of anisotropy in 3D medical imaging, where isotropic 3D convolutions fail to adequately capture differential information densities across dimensions. As demonstrated in pdf.Tab.4, our experiments on 3D U-Net, 3D U-Net with 2D uC, and 3D U-Net with 3D uC underscore the efficacy of 2D convolutions in enhancing slice-wise feature extraction. uC offers a solution to anisotropy in 3D medical imaging. Additional details on our methodology will be included in the final version of the paper.\nW3: Additionally, the reference to 3D convolutions...\nIn the description on lines 194-195, uC1-uC3 represents a simplified 2D U-Net structure rather than a 3D U-Net structure. We apologize for this oversight. We will provide further details on the uC 3D U-Net architecture and the blocks used in Fig.2 and its caption in the final paper version.\n\nW-Experiments:\n>W4: To robustly support the choice of a 2D UNet...\n\nA comparative evaluation involving 3D U-Net, 3D U-Net with 2D uC and 3D uC is detailed in pdf.Tab.3. Figure 2 further isolates the impact of 2D and 3D convolutions within the uC modules by removing the influence of skip connections, thereby attributing observed performance differences directly to convolutional efficacy, where 2D convolution demonstrates superior slice-wise feature extraction compared to 3D. This finding is reinforced by image reconstruction experiments (pdf.Tab.4), where 2D convolution outperforms 3D in image details. The evaluation of 2D uC applied across arbitrary planes (pdf.Tab.2) consistently enhances 3D U-Net's performance, suggesting a suboptimal exploitation of planar information within the three-dimensional architecture.\n>W5: Challenging datasets.\n\nWe validated our proposed method on the BTCV standard dataset (pdf.Tab.5). Using 30 annotated cases for training and validation, the method demonstrated significant efficacy. Given the smaller dataset size, CNN-based models outperformed Transformer-based models, with more details to be included in the revised paper.\n>W6: The number of parameters for SegResNet.\n\nThe baseline data in pdf.Tab.1 was sourced from the 3D UX-Net paper, which used a smaller initial channel count (8) for SegResNet, resulting in lower parameters and FLOPs. Conversely, pdf.Table 4 reflects a larger initial channel count (32) for enhanced performance, aligning SegResNet's parameters with 3D U-Net.\n>W7: Since the addition of the DFi...\n\nFuture versions will include more details of the DFi module. Unlike U-Net's skip connections, which merge encoder features during upsampling via concatenation and subsequent convolutions, the proposed DFi module integrates addition, concatenation, and convolution layers for efficient 2D and 3D feature fusion with reduced computational load. Experimental results in pdf.Tab.6 indicate that DFi offers a slight performance improvement with lower parameter and FLOPs counts compared to direct concatenation. The paper's focus on DFi will shift towards feature fusion efficiency rather than effect. Continuous improvement efforts will aim to enhance 2D and 3D feature integration without increasing computational burden.\nW-Minor comments:\n>W8: Minor comments: Fix the AdamW citation in line 246.\n\nThe AdamW citation in line 246 will be modified in the future version.\n\nThank you once again for your thoughtful review, which has greatly encouraged us! We hope that our revisions will address all your concerns and clear up your concerns.\nヽ（＾ω＾）ノ", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "c63fk7tfJL", "reviewer": "Reviewer_J8Kk", "summary": "The manuscript proposes using 2D U-Net connections to enhance 3D U-Net segmentation. The innovative approach demonstrates excellent performance across different imaging modalities and achieves state-of-the-art (SOTA) results with fewer parameters and lower computational costs on four public datasets. The manuscript thoroughly describes the challenges associated with 3D segmentation.", "questions": "1. Can authors discuss more about the principle of DFI?\n2. Why is the HD95 metric for +cU(stage 1, 2, 3)+DFI slightly higher than for +cU(stage 1, 2, 3)?", "limitations": "The impact of DFI on various 3D segmentation backbones is not explored.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The creative use of 2D U-shape connections in a 3D network shows impressive performance.\n2. The method achieves SOTA performance with fewer parameters and lower computational costs across four public datasets.\n3. The manuscript provides a comprehensive description of the challenges encountered in 3D segmentation.", "weaknesses": "1. The principle behind the proposed DFI is not well-explained.\n2. In the ablation studies section, the HD95 metric for +cU(stage 1, 2, 3)+DFI is slightly higher than for +cU(stage 1, 2, 3).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful comments to our work! We appreciate the opportunity to address your concerns and respond to your questions.\n\n**W1 & Q1:**\nThe motivation for employing DFi and its underlying principles can be futher discribed: In conventional 3D U-Net architectures, skip connections integrate the original feature information from the encoder during the upsampling process through cat, followed by two 3x3 convolutions to reduce the number of channels and consolidate useful information. However, this rudimentary feature fusion approach may lead to inefficiencies when reconciling the disparity between 3D spatial features and 2D slice plane features. Therefore, we devise a streamlined, efficient multi-scale feature fusion module DFi. The 2D slice plane features extracted by uC and the 3D spatial features from the 3D U-Net encoder are cat to retain maximal original feature information.                     Howerver, subsequent convolution operations to reduce channel numbers are computationally intensive and inefficient. Utilizing addition for feature fusion could result in information loss due to inherent discrepancies between the two types of features.                     Hence, an approach combining cat, addition, and subsequent convolution layers, with the aim of minimizing parameter count and FLOPs, resulting in the design of the DFi module.\nFor the 2D slice plane features F1 extracted by uC and the 3D spatial features F2 obtained during 3D U-Net upsampling, branch 1 of the DFi module cats F1 and F2, then employs a 1x1 conv to halve the channel count, preserving salient features to produce feature map Fc. Branch 2 initially extracts critical information from F1 and F2 using 1x1 convs, followed by addition and sigmoid activation to generate the attention map Fa. The element-wise multiplication of Fc and Fa adjusts and integrates the feature map Fc from branch 1, thereby learning the relative importance of F1 and F2 during the fusion process.\nThe proposed DFi module integrates and simplifies addition, cat, and subsequent\nconvolution layers, achieving a more lightweight and efficient fusion of 2D and 3D spatial features. Additionally, we conducted supplementary experiments to validate the efficacy of DFi and compared its parameter count and FLOPs, as shown in pdf.Tab.6.                                 The experimental results indicate that employing DFi, as opposed to direct cat, offers a performance improvement with a reduced parameter count and FLOPs, thereby justifying  its replacement of the concatenation operation in the 3D U-Net is helpful.\n\n\n**W2 & Q2:**\nAs you noted, while DFi induces a slight increase in HD95, accompanied by modest improvements in mIoU and Dice metrics, the overall performance impact remains positive. This is corroborated by the additional experiments presented in pdf.Tab.6. Given that DFi offers a computationally efficient feature fusion method in the uC 3D U-Net, its substitution for concatenation is advantageous.\n\nWe are truly grateful for your insightful comments and the time you have invested in improving the quality of our U-shaped Connection. The feedback is highly motivating, and we appreciate your valuable comments! ヽ( ´▽` )ノ", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1xTmKQVQI2", "reviewer": "Reviewer_7CRU", "summary": "This paper proposes a novel element for 3D medical image segmentation, called uC (U-shaped connection) that replaces standard skip connection with 2D convolutions.", "questions": "The authors claim their code is available on github. However, I did not find the code, nor a link at https://anonymous.4open.science or some similar one. \n\nLine 246, the reference to AdamW is missing. \nLine 182, why 5D volumetric data image?", "limitations": "The authors have not highlighted any limitations, nor potential negative societal impact.", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- uC 3DU-Net shows state-of-the-art results on 4 datasets. \n- It reduces computational complexity and number of parameters\n- It is aimed at addressing the axial-slice performance drop-off in 3D convolutions", "weaknesses": "- Paper is hard to read at times. The language is sometimes complex, using rare words I have never seen used in that context. \n- Section 3.2 is essentially empty: there is no clear description of the U-Shaped connection.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the thoughtful review of our work! Please allow us to address your concerns and answer the questions.\n\n>W1: Paper is hard to read at times. \n\nYour suggestions are pivotal, we will comprehensively optimize the readability of the manuscript. \n>W2: Section 3.2 is essentially empty...\nSection 3.2 has been reorganized and rewritten to reduce unnecessary background information and introduce more detailed descriptions of U-shaped Connection(uC), as follows: \n\nConsidering the objective of skip connections to capture detailed features of original images, it is essential to note that 3D medical images fundamentally represent a sequence of 2D images. Given the anisotropic nature of medical images, the 2D slice plane shows richer feature information compared to the temporal sequence axis. Although basic skip connection methods like cat and addition can supplement original feature information to some extent, these methods fail to fully utilize the rich slice plane information in 3D medical images. To address the challenges posed by the anisotropy of 3D medical images, the U-shaped Connection(uC) is proposed. This approach employs a simplified 2D U-Net to implement skip connections, thereby supplementing the rich original 2D axial plane feature information, and enhancing the 2D axial plane feature extraction capabilities of any 3D image segmentation network. The uC combines features extracted by 2D convolution with those extracted by 3D upsampling, offering more efficient feature extraction compared to pure 3D convolution, as detailed structure in Sec3.3.\n\nThe fundamental structure of uC is based on a 2D U-Net.  In comparison to the basic 2D U-Net, uC omits the initial and final conv layers, retaining only the downsampling and upsampling layers to minimize computational load and enhance the extraction efficiency of original slice plane features.  Each downsampling layer comprises an average pooling layer and two conv layers, each consisting of 2D conv, Group Normalization (GN), and ReLU.  The average pooling layer reduces the feature map size by half, and the subsequent conv layers double the channel number, with the group number of GN set to half the current channel number.  Each upsampling layer includes a transposed convolution layer and two conv layers, where the transposed convolution layer restores the feature map to its original size, and the conv layers reduce the channel number back to its half.\n\n>Q1: The code.\n\nWe have provided the project code on anonymous github and sent the anonymous link to AC.\n>Q2: The reference to AdamW is missing. \n\nYour meticulous review is greatly appreciated. The AdamW citation on line 246 will be rectified in the future version. \n\n>Q3: why 5D volumetric data image?\n\nThank you for the valuable feedback regarding the description of 5D volumetric data image, which should indeed be 3D volumetric data image on line 182, we will rectified it in the future version.\n\nYour comments have greatly encouraged us, and we are sincerely grateful for the significant help you have provided in refining our manuscript.~ （ ´▽` )ノﾞ", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
Dear Reviewers,

The authors extend profound gratitude for the meticulous time and expertise invested in reviewing our manuscript. The invaluable insights and critiques provided have been pivotal in guiding comprehensive revisions. In response, substantive modifications have been executed, meticulously addressing all raised concerns in a scholarly manner. Specifically, to Reviewers Lrjd and WWao, we earnestly request a reevaluation of your decisions, predicated upon the thorough efforts undertaken to address your key comments.

In the ensuing responses, each query and concern is meticulously addressed. Numerous extra experiments were conducted, and a PDF comprising the resultant figures has been appended. Table 1 elucidates the performance of D-LKA-former and UNETR++ across four datasets (FLARE2021, FeTA2021, OIMHS, AbdomenCT-1K), providing a comprehensive comparison with attention-based methods. Table 2 assesses the impact of various slicing approaches on model segmentation performance, demonstrating the efficacy of 2D slicing augmentation in enhancing performance across any plane. Figure 1 visualizes the validation curve with Dice Score for 3D U-Net, 3D U-Net+3D uC, and 3D U-Net+2D uC during training, revealing faster convergence and higher performance ceilings with the addition of 2D uC. Tables 3 and Figure 2 delineate the performance across different channel configurations of 3D U-Net, 3D U-Net+3D uC, and 3D U-Net+2D uC on the OIMHS dataset. By substituting the 2D uC module with 3D uC, a performance evaluation of 2D versus 3D convolutions within the same structure is enabled, uncovering better feature extraction capability of 2D convolutions. Image reconstruction experiments were also conducted, and Table 4 evaluates the slice-wise image reconstruction performance of the three models, where the 2D uC model outperforms all 3D convolution-based structures, substantiating the enhancement of planar features by 2D convolutions. The evaluation of the BTCV standard dataset was incorporated to further substantiate the robustness of the proposed method, with results depicted in Table 5. Additional experiments were conducted, as shown in Table 6, to validate the robustness of the DFi conclusions.

These comprehensive experiments collectively underpin the argument that 2D convolutions exhibit good feature extraction capabilities in every plane compared to 3D convolutions. The utilization of uC structured convolutions demonstrably enhances performance, with planar feature augmentation consistently yielding improved outcomes. Given the richness of information on image planes, employing 2D uC for planar enhancement attains optimal performance.

Once again, we express our sincere appreciation for your valuable contributions to the review process. Your expertise and guidance have significantly enhanced the quality of our work and the robustness of our conclusions.

Meta-review: This paper provides a an approach to utilize 2D features via 2D skip connections for 3D CNN U-net segmentation.
The reviewers all appreciate the paper, in particular the simplicity of the idea combined with convincing experiments.
The authors should take care to include their code in the final paper, as well as to utilize the detailed feedback from the reviewers to improve their paper.
Decision: Accept (poster)