One-to-Multiple: A Progressive Style Transfer Unsupervised Domain-Adaptive Framework for Kidney Tumor Segmentation
Authors: Kai Hu
Jinhao Li
Yuan Zhang
Xiongjun Ye
Xieping Gao
Venue: NeurIPS 2024 poster (2024)
Keywords: Unsupervised Domain Adaptation
Multi-sequence MRI Segmentation
One-to-Multiple
Progressive Style Transfer
Primary area: machine_learning_for_healthcare

Abstract: In multi-sequence Magnetic Resonance Imaging (MRI), the accurate segmentation of the kidney and tumor based on traditional supervised methods typically necessitates detailed annotation for each sequence, which is both time-consuming and labor-intensive. Unsupervised Domain Adaptation (UDA) methods can effectively mitigate inter-domain differences by aligning cross-modal features, thereby reducing the annotation burden. However, most existing UDA methods are limited to one-to-one domain adaptation, which tends to be inefficient and resource-intensive when faced with multi-target domain transfer tasks. To address this challenge, we propose a novel and efficient One-to-Multiple Progressive Style Transfer Unsupervised Domain-Adaptive (PSTUDA) framework for kidney and tumor segmentation in multi-sequence MRI. Specifically, we develop a multi-level style dictionary to explicitly store the style information of each target domain at various stages, which alleviates the burden of a single generator in a multi-target transfer task and enables effective decoupling of content and style. Concurrently, we employ multiple cascading style fusion modules that utilize point-wise instance normalization to progressively recombine content and style features, which enhances cross-modal alignment and structural consistency. Experiments conducted on the private MSKT and public KiTS19 datasets demonstrate the superiority of the proposed PSTUDA over comparative methods in multi-sequence kidney and tumor segmentation. The average Dice Similarity Coefficients are increased by at least 1.8% and 3.9%, respectively. Impressively, our PSTUDA not only significantly reduces the floating-point computation by approximately 72% but also reduces the number of model parameters by about 50%, bringing higher efficiency and feasibility to practical clinical applications.

One-to-Multiplee A Progressive Style Transfer Unsupervised Domain-Adaptive Framework for Kidney Tumor Segmentation
Kai Hu
Jinhao Li Xiangtan University jhl i aww.ammmmm.....................e xtu. edu. cn
Yuan Zhang' Xiangtan University yuanzOxtu.ede cn
Xiangtan University
kaihu@xtu, edu.
Xiongjun Ye
Xieping Gao' Hunan Normal University xpgao@hunnu. edu,
Cancer Hospital, Chinese Academy of Medical Sciences yexiongjun@ci www.:junnncciscc.ccm cams. ac.
Abstract
In multi-sequence Magnetic Resonance Imaging (MRI), the accurate segmenta- tion of the kidney and tumor based traditional supervised methods typically necessitates detailed annotation for each sequence, which both time-consuming and lubor-intensive Unsupervised Domain Adaptation (UDA) methods can effec- tively mitigate inter-domain differences by aligning cross features, thereby reducing the annotation burden. However, most existing UDA methods are lim- ited to one-to-one domain adaptation, which tends to be inefficient and resource- intensive when faced with multi-target domain transfer tasks. To address this challenge, we propose novel and efficient Pne-to-Multiple Progressive Style Transfer Unsupervised Domain-Are Domain-Adaptive (PSTUDA) framework for kidney and tumor segmentation in multi-sequence MRI. Specifically, we develop multi-level style dictionary to explicitly store the style information each target domain various stages, which alleviates the burden of single generator in multi-targe transfer task and enables effective decoupling of content and style. Concurrently, employ multiple cascading style fusion modules that utilize point-wise instance normalization to progressively recombine content and style features, which en- hances cross- modal alignment and structural consistency. Experiments conducted on the private MSKT and public KiTS19 datasets demonstrate the superiority of the proposed PSTUDA over comparative methods in multi-sequence kidney and tumor segmentation. The average Dice Similarity Coefficients are increased by at least 1.8% and 3.9%. respectively. Impressively, our PSTUDA not only significantly reduces the floating-point computation by approximately 72% but also reduces the number model parameters by about 50%, bringing higher efficiency and feasibility to practical clinical applications.
Introduction
Kidney tumor segmentation is a crucial task in medical image analysis, playing an essential role in the diagnosis, staging, and treatment kidney cancer computed tomography (CT) images 4
3. Previous studies have primarily focused on
5
6,
7 g 9 but recent years, magnetic resonance imaging
(MRI) has emerged as a safer alternative due its non-radiative nature [10 [2]. Compared CT.
"Corresponding authors.
"Code publicly available at: https//ggiiabbbcoommMMM////SISS
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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MRI offers superior contrast for characterizing kidney tissue and tumors, enabling clearer distinction between normal and pathological tissues. Furthermore, the malti-parameter imaging capability of that enhances the comprehensive description of pathological features. These advantages have established MRI as the preferred modality for clinicians in diagnosing kidney disease, particularly kidney tumors, and have facilitated the widespread use of multi-sequence MRI in clinical practice for kidney and tumor segmentation.
MRI generates diverse sequences 11 14
13
In existing studies, traditional supervised segmentation methods typically rely on large amount of annotated data, and performing fine- ane-grained annotations for single sequence already time- consuming and inteeniiteesive task, let alone for every sequence. Unsupervised domain adaptation (UDA) methods [16] [7 [8] 19, 20] have been proposed as a promising solution to address these challenges. Current UDA methods mainly focus on one-to-one domain adaptation, involving single source domain and a target domain. Although these methods perform well in certain scenarios, they lack scalability, meaning that for multi-targes domain tasks, training paired generators for each source- target domain pair results in significant computational costs and resource consumption. Furthermore, one-to-one domain adaptation methods can only learn fixed mappings between two domains, thereby failing to capture the potential commonalities and connections among multiple sequences.
As an initial attempt to apply multi-domain adaptation technology to medical imaging, Xu et al. (21] proposed OMUDA for abdominal organ segmentation using multi-sequence MRI. However, the architecture of OMUDA is derived from StarGAN v2 [22], which was originally designed for style transfer in natural images and has not been specifically tailored and optimized for the needs of domain adaptation in medical imaging. Although it shows improvement in resolving domain confusion across multiple domains, this limitation leads to suboptimal generated images with regards to organ structural consistency and detail preservation. Consequently, the task of one-to aee-to-multiple domain adaptation in medical imaging remains a significant challenge, necessitating more refined and specialized approaches for further advancement.
In this paper, we propose a One-to-Multiple Progressive Style Transfer Unsupervised Domain- Adaptive (PSTUDA) framework, which explicitly stores multi-level style features from different domains in designated multi-level style dictionaries, thus alleviating the burden on the generator and achieving the decoupling of content features from style features. Our PSTUDA employs multiple cascaded style fusion modules to recombine multi-level style features from different domains with content features layer by layer using Point-wise Instance Normalization (PIN), thereby ensuring that the generated images across multiple target domains have high-quality style and structure.
The main contributions of our work are summarised as follows:
We explore a novel and efficient One-to-Multiple Progressive Style Transfer Unsupervised Domain-Adaptive (PSTUDA) framework, which is capable of simultaneously transferring a single annotated source domain to multiple unannotated target domains, significantly reducing the need for tedious domain adaptation work for each target domain.
We introduce a multi-level style dictionary that stores style information for each domain at different stages of style transfer, alleviating the burden the generator to perform multiple tasks and effectively decoupling content features from style features.
We propose a progressive style transfer paradigm and a Point-wise Instance Normalization (PIN) method. The former comprises multiple cascading style fusion modules, each recom- bining content features with corresponding style features through PIN, thereby achieving better cross-modke alignment and structural consistency.
We construct a multi sequence kidney tumor MRI dataset called MSKT to facilitate research on kidney tumor analysis. Quantitative and qualitative results on the MSKT and the public dataset KiTS19 show that our PSTUDA framework outperforms the state-of-the-art methods and significantly improves segmentation performance and training efficiency.
2 Related Work
2.1 Kidney Tumor Segmentation
Computer-aided diagnostic methods for kidney tumor segmentation play a crucial role clinical practice [23 24 25 26 27 The significant variability in the size, shape, and location of kidneys and
2

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tumors presents a considerable challenge to accurate segmentation. Yu et al. [28 developed Crossbar- Net to capture global and local features of kidney tumors through crossbar patches and focused on difficult-to- -oteemeett--semment regions through a cascade training strategy. Myronenko et al. [29 designed a dedicated boundary branch supervised by an edge-aware loss term to enhance the consideration of organ and tumor edge information. These approaches are centered on improving the ability of the model to recognize the complex morphology of kidney tumors. To further advance this effort, Pandey et al. [30] integrated active contouring with 3D-U-Net to achieve precise delincation of kidney tumor shapes, showcasing the potential of merging deep learning with traditional image processing techniques. However, these approaches typically rely on fully supervised learning with extensive pixel-level annotations. To address this limitation, researchers are exploring alternative solutions in the fields of remi-superrvisee
self-suuprvised and unsupervised learning. Ma et al. [I] introduced an Affinity Network that learns from limited data using k-nearest neighbors attention pooling layers. Similarly, Ciga et al. (32) and Faust et al. (33] developed methods that enhance feature learning and guide tumor analysis through self-supervised and unsupervised techniques, respectively. By reducing
the reliance annotated data, these methods not only lower costs but also improve the model's generalization capabilities.
2.2 Unsupervised Domain Adaptation
UDA is one of the important methods for addressing domain difference. aims to transfer a model from an annotated source domain to an unannotated target domain. Existing works have mostly focused on one-to-one domain adaptation
39,, yielding impressive outcomes. For example, CycleGAN (3 used cycle consistency constraints to transform unpaired images from one domain to another. CYCADA 40 enforced cycle consistency by combining methods of image space alignment and latent representation space alignment. MUNIT [41 decomposed image representations into content and style codes, enabling multimodal image translation. For medical image domain adaptation, SIFA (35 achieved domain alignment from both image and feature perspectives, enabling the segmentation network to adapt to the unannotated target domain. Thereafter, DEPL (39] further improved the segmentation accuracy by employing multi-source domain extension and selective pseudo-labelling strategies.
34
35 36
However, these one-to-one domain adaptation methods lack scalability when handling multiple domain transfer tasks, as they can only learn the relationships between two different domains at a time. StarGAN [22 performed image-to-image translation across multiple domains using a single model, and then the improved version, StarGAN v2 [22) further enhanced diversity in generated images by introducing style codes specific to each domain. Additionally, Sharma and Hamarneh 43 proposed a multi-modal generative adversarial network that leveraged redundant information from available sequences to synthesize missing MRI pulse sequences in patient scans. Gholami et al. 44 developed an information -theorrticc approach that aimed to find shared latent spaces for domain adaptation across multiple target domains. It should be noted that these methods predominantly focus on the diversity of the generated images, while paying less attention to the structural consistency before and after image translation.
2.3 Normalization of Image Translation
In UDA tasks, image normalization is a key step that can help models to learn and transfer features effectively 45
49 50) Instance Normalization (IN) [5T] normalizes the features of each sample to improve the quality and realism of generated images. Adaptive Instance Normalization (AdaIN) [52] dynamically adjusts the relationship between style and content features of input images to enable a rapid transformation across arbitrary styles. Batch-Instance Normalization (BIN) 53 explicitly normalizes unnecessary style variations in images while preserving useful styles. AdaAttN 54 introduces adaptive attention normalization to optimize the effects of arbitrary neural style transfer. GramLIN (55] continuously measures the proximity of the current stylized output to the target style to achieve progressive stain transfer. In medical image domain adaptation tasks, it is crucial to maintain structural consistency during image translation, in addition to changing image styles. Therefore, we propose novel normalization method called PIN, which progressively fuses content and style features at each pixel by considering image details and local style differences. The goal is to ensure selutstructured images after transfer.
46
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Figure 1: (a) The overall architecture of the proposed One-to-Multiple Progressive Style Transfer Unsupervised Domain-Adape The generator is composed of an encoder, a decoder, and multiple style fusion modules. (b) shows the progressive style transfer process, achieved through cascaded style fusion modules.
framework, which includes shared generator and discriminator.
Method
3.1 Overview of the proposed One-to-Multiple Framework
Formally, for a one-to-multilee domain adaptation task, we have one source domain S and multiple target domains T:. The source domain is annotated, denoted as = {(x,, 3;)1 while the target domains are unannotated, denoted T; {- } Each domain is assigned domain label, for example, == [0. 1, .] representing the source domain 0, target domain 1, etc., up target domain N. Figure aa) illustrates
PSTUDA framework, which mainly consists of shared generator (including an encoder, decoder, and multiple style fusion modules) and multi- scale discriminator. X Xtrge and X recon represent the input image from the source domain, the generated pseudo-target domain images corresponding to the input image, and the image reconstructed back the source domain from the pseudo-targe target domain images, respectively. Our task involves two stages. The goal of the first stage is to train a single generator G such that given source domain image X and any target domain label 4, can generate a pseudo-target domain image Xtrg. corresponding to the image X i.e., G(X L;) + Xirg.i- The objective of the second stage is to utilize the generated pseudo-target domain images Xtrgs and source domain annotation y to train segmentation network to achieve accurate segmentation of the kidney and tumor.
3.2 Multi-level Style Dictionary
Our design is inspired by the StarGAN v2 multi-doman image generation framework, which employs a Mapping Network and Style Encoder obtain style encodings. The Mapping Network transforms random Gaussian noise into diverse style encodings, while the Style Encoder extracts style encodings from reference images. These techniques are essential for transforming natural image styles and enhancing artistic variety. However, in medical image domain adaptation, they encounter challenges in extracting comprehensive style features. Style encodings from a single image may not capture the full range of styles within domain, and deriving styles from Gaussian noise, while introducing

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diversity, can result in instability. In medical image analysis, such instability could potentially compromise feature recognition and segmentation accuracy.
To address these challenges, we propose to define a set of learnable multi-level style dictionaries for each domain, where the style information each level closely corresponds to the respective phase of style fusion. This multi-level structure allows for starting with basic style features and exploring more complex layers of style, thereby providing a progressively refined path for the style fusion process. During the generative adversarial process, the continuous updating and learning of the style dictionary can adapt to the ever-changing style demands. The early-stage stylized features will provide feedback for subsequent style feature learning. thus guiding them to update in dynamic and targeted manner. By learning style information from the whole domain, the style dictionary becomes more representative, which effectively overcomes the limitation of extracting style encoding from a single image. Moreover, the iterative updates of style dictionaries enhance stability and reduce uncertainty in extracting style encodings from random noise. Storing style encoding information in a multi-level dictionary helps alleviate burden on the generator and achieves effective decoupling of content features and style features. This allows it to focus on capturing domain-invarian features such as structure and shape. In this way, the multi-levels style dictionary refines complex styles within target domains at each level, ensuring that each can timely reflect latest progress in style transfer process more precise and coordinated.
3.3 Progressive Style Transfer Paradigm
In PSTUDA, multiple cascaded style fusion modules and decoders constitute the core components of progressive style transfer. Figure b) illustrates the main processes of the first and last (decoding phase) style transfer stages, with similar style transfer processes in the intermediate stages. In the first style transfer stage, the style fusion module requires two inputs: the content feature X1 E RR XX obtained by downsampling from the encoder, and the first-level style encoding V1 E SXSXI from the target domain, matched with the current style fusion module. We utilize the domain label L multiplied by the first style dictionary to select the target style encoding Vi. First, the content feature X, passes through convolutional layer to obtain the content feature X and the target style encoding VI undergoes two consecutive 1 x 1 convolutions for channel transformation to obtain the style feature V/2 E I XC
After obtaining the content feature X, and style feature Vi, we propose a novel style fusion normal- ization method, Point-wise Instance Normalization (PIN), to effectively combine the two for more subtle and accurate pixel-level style transfer. The PIN can be defined as:
PIN Vi = "choo (V)) N (orm(&)) + Behwo (V)),
Vehow Vi), Betoon (V) = Chunk (h(2)))w))))
(2)
h(2e)bor Cone (Vi).
where Norm(-) denotes the Instance Normalization of the content feature X₁ HXC at layer I. The parameters Tohw and Behw are scaling and shifting parameters specific to the target domain. These parameters are derived by applying
Block and Chunk operations to the style feature Vi " and are adjusted to match the spatial dimensions of the content feature Xt, thus enabling independent style transfer at each pixel. By rescaling the feature map using these parameters, style information specific to the target domain is integrated into the feature map for style transfer. PIN provides unique style statistics for each spatial point of the content feature, allowing for customized style fusion based on different regions and features of the image content. This facilitates finer local style variations and richer style details, making it particularly suitable for applications requiring precise style adjustments, such as medical image segmentation.
In the final stage of style transfer, the decoder takes the stylized content feature Xn, E "3'''x""xC" xc' from the previous layer and the last-level style encoding V,, E R SxS11 as input. To spatially match the content feature X,, the style encoding V,, first undergoes upsampling through deconvolutional layer for scale transformation, followed by two consecutive 1 x 1 convolutions for channel trans- formation, resulting in the style feature V, E R xC' The other style fusion operations are similar to those in the first stage. There are two considerations for performing style transfer during the decoding phase. Firstly, the decoding phase is the process of image reconstruction, where integrating
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Figure 2: Architecture of the Multi-Scale Discriminator, composed of multiple residual blocks.
style features can effectively incorporate high-level style information from the target domain into the image. This helps better express the target style while preserving content information, especially in terms of details and textures. Secondly, as the decoder is responsible for upsampling, has the opportunity to refine and restore image details while enlarging the feature maps. Introducing style transfer at this stage ensures that these details not only conform to the content structure but also match the characteristics of the target style, thus achieving effective fusion of content and style at different scales.
3.4 Generator and Discriminator Architecture
Generator As previously mentioned, the generated pseudo-target domain images will be used in conjunction with the corresponding segmentation annotations from the source domain to train the subsequent segmentation networks. Therefore, it is crucial to maintain structural consistency during the image translation process for the success of downstream applications. The generator in StarGAN v2, due to its multiple downsampling steps, unfortunately leads to a loss of spatial information, which poses challenges maintaining structural consistency during image translation. In light of CycleGAN's outstanding performance in one-to-one medical image translation, the approach of OMUDA by integrating the generator architecture of CycleGAN into our encoder and decoder. To facilitate single generator to handle onc-to-multiple transfer tasks, we replace the IN layers in the decoder with PIN layers. The complete generator comprises an encoder, a decoder, and a series of cascaded style fusion modules. Among them, the encoder, equipped with two downsampling layers and multiple IN layers, is tasked with extracting domain Nomain-invarian features from the input image of the source domain. The cascaded style fusion modules are responsible for integrating the style features of the target domain with the decoder is composed of two upsampling layers embedded with PIN and is responsible for style transfer and image reconstruction during the decoding phase.
adopt
features of the source domain. The
Discriminator Inspired by Wang et al's work [56), we enhance the original discriminator archi- tecture of StarGAN v2 with a multi-scale mechanism. As depicted in Fig. 2 the discriminator is composed multiple independent discrimination branches, each containing four residual blocks [57], with each residual block having N output branches serving N specific target domains. The input to the discriminator includes not only the original image but also images processed through different scales of downsampling. The multi-branch output of the discriminator first multiplies with the target domain label L to select the discrimination output for the corresponding domain before proceeding authenticity determination. By evaluating the generated images various scales, the multi-scale discriminator can more comprehensively judge the realism of the images, thereby encouraging the generator to produce images with higher quality. More detailed information on the generator and discriminator architectures can be found in Appendix B.
3.5
Loss Function
For convenience, give the following symbol definition. 2, E X denotes source domain image, with its corresponding source domain label 1, E L. le E L represents any target domain label. G and D stand for the generator and diseriminator, respectively. The loss functions in the generator include adversarial loss Lade. cycle consistency loss Leyes and identity loss Lids (58). The loss the discriminator includes adversarial loss Ladv. Among them, the adversarial loss is defined
Lade Er.,, [log(1 - D(G(x,, 44),4) Ez. (e))
(4)

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which encourages the generator to generate images that are indistinguishable from the target domain images by the discriminator. The cycle consistency loss is computed as
Laye Le [
=x,1l,,
which ensures that an image translated from the source domain to the target domain can be translated back to the original image, thus preserving the structural consistency of the image throughout the translation process. The identity loss can be defined as
Lidt = E t, III (G (.) - xs 1l1
(6)
which is used to preserve the content of the source domain images when the generator is applied to them. The complete training loss can be summarized as follows:
min max Ladv + Aeye + Xidt Lidt.
G D
where Aeye and Aide denote the weights for the corresponding loss terms and they are set to 10 and 1 in our experiments, respectively.
Experiments
4.1 Dataset
In this study, we utilize two datasets for the performance evaluation of the method. The first one is a private dataset named MSKT, which comprises 104 cases, each including four sequences: Tlc, FS T2W, T2W, and DWI. The second is the publicly available KiTS 19 [59] dataset. We conduct our first set of comparative experiments using the MSKT dataset. Specifically, the annotated Tic data is served as the source domain, while the unannotated FS T2W, T2W, and DWI data are considered as the target domain. For our second set of comparative experiments, we combine KiTS19 with the MSKT dataset. In this case, all training data from KiTSI9 19 are used as the source domain, and all four sequences of the MSKT are considered as the target domain. We ensure that there is no overlap between the source domain, target domain, and test set to prevent any potential information leakage. More details about the dataset and evaluation metries are provided in Appendix A
4.2 Comparative Study
To comprehensively evaluate the performance of the proposed method, we compare PSTUDA with five other state-of-the-art UDA methods on the MSKT and KiTS19 datasets: CycleGAN (34), MUNIT [41]. SIFA (351, DEPL (3)], and StarGAN v2 122.. Except for SIFA and DEPL, the others are limited to cross- cross-domain domain image transfer. Therefore, we train a dedicated U-Net 600 from scratch based on the pseudo-targe -tagett domain images generated by these methods for multiple sequences. For a fair comparison, all hyperparameters in the U-Net remain consistent. Due the one-to-one image transfer characteristics of SIFA, DEPL., CycleGAN, and MUNIT, we train separate model for each sequence using these methods to handle multi- sequiee MR image transformations.
Due to the unpaired nature of multi-sequence MR images, it is challenging to quantitatively assess the style effects and structural consistency of the generated images. Experience suggests that if anatomical structures are distorted during the image transfer process, the pseudo-target domain images generated will not correspond with the annotations of the source domain, thereby impairing segmentation performance. Moreover, if there is significant difference between the style of the generated images and the real target domain images, this discrepancy in data distribution may also detrimentally affect the segmentation results. Hence, the quality of the generated images is indirectly indicated by the segmentation results.
Results on MSKT Table I presents the Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HDys) results for all methods across different MR sequences. The average DSC indicate that our PSTUDA outperforms other methods in segmentation performance on all MRI sequences. We also observe that PSTUDA significantly surpasses MUNIT, SIFA, DEPL, and StarGAN v2 from both DSC and HDg5- Although SIFA and DEPI. are more efficient end-to-end domain adaptation segmentation methods, they do not perform well in our task. The images generated by these methods exhibit structural distortions and lack naturalness t4 and 5th columns in Fig. 3) Particularly, DEPL
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Source
CyccGAN
MUNIT
StarGA
PSTUDA
The
4
ES T2W
Tlc
T2W
Tic 4 DW1
Figure 3: Qualitative results for Tle FS T2W, T2W, and DWI the MSKT dataset. Blue and red bounding boxes indicate the annotated boundaries of the kidney and tumor, respectively (Same below).
shows severe structural distortion of kidneys and tumors in the T2W sequence, which has greater inter-domain differences. MUNIT and StarGAN v2, while excelling in the diversity of generated images, fall short in maintaining cross-domain structural consistency, leading to distorted structures in the generated images (3rd and 6th columns in Fig. 3). The performance of CycleGAN is close the T2W sequence,
to that of PSTUDA FS T2W and DWI sequences, but it underperforms as the translated images lose many important structural details, resulting in poor image alignment. In contrast, PSTUDA prioritizes structural consistency in the cross-domain image translation process by separating content from style and progressively recombining content and style features in point-wise manner. This approach produces images with higher fidelity, as demonstrated in the last column of Fig. 3 thereby significantly enhancing segmentation performance.
Table 1: Quantitative segmentation results of different comparative methods on the MSKT dataset.
TIc FFS T2W
Tle >22
Tic >WW
Metrics
Methods
Kidncy
Tumor
Avg.
Kidney
Tumor
Avg.
Kidney
Tumor
Avg.
Supervised training
90.14
88.73
89.44
87.53
80.68
84.11
90.20
84.76
87.48
W/o aduptation
60.66
49.69
55.18
0.71
28.29
14.50
54.44
35.30
44.87
CycleGAN 33
86.50
74.00
80.25
71.97
51.94
61.96
87.60
71.88
79.74
MUNIT 41
80.96
53.00
66.98
75.78
48.34
62.06
78.29
51.87
65.08
DSC (%)
SIFA 35
78.31
50.58
64.45
65.54
37.10
51.32
72.94
43.28
58.11
DEPL
39
85.22
68.20
76.71
23.44
23.16
23.30
83.63
64.26
73.95
StarGAN
22
73.58
41.77
57.68
42.87
28.68
35.78
75.26
41.29
58.28
PSTUDA(Ours)
86.30
76.36
81.33
77.26
53.77
65.52
86.99
74.23
80.61
Supervised training
2.47
1.78
2.13
3.49
8.25
5.87
2.04
3.36
2.70
W/o adaptation
11.94
43.03
27.48 .7.48
40.14
69.39
54.77
11.38
33.60
22.49
CycleGAN 34
4.92
31.54
5.37
6.34
5.86
4.57
14.60
MUNIT 41
6.08
31,95
19.01
7.36
25.58
16.47
6.43
39.09
22.76
HDas (mm)
SIFA 35
6.12
53.60
29.86
10.48
57.18
33.83
14.25
54.22
34.24
DEPL
39
6.75
36.87
21.81
17.65
70.65
44.15
11.14
25.43
18.28
StarGAN 22
5.39
33.20
19.29
15.43
68.98
42.20
13.70
26.84
20.27
PSTUDA(Ours)
4.66
25.85
15.25
6.81
21.37
14.09
2.96
34.65
18.80
implics that report the results of
according official code.
implies the method based on our implementation.
Results on KITS19 and MSKT This experiment utilizes CT images from KiTS19 as the source domain and four MRI sequences from MSKT as the target domain. Given the different imaging technologies employed by CT and MRI, the domain difference between them is significantly greater than that between the various MRI sequences. As shown in Table 2 most comparative methods show reduction in kidney and tumor segmentation performance on T2W, T2W, and DWI when compared to the first set of experiments. Although the average DSC of CycleGAN on the DWI is higher than that PSTUDA (by about 1.9%). ) its performance lower than that PSTUDA by approximately 7.5% and 9.1% on FS T2W and T2W. respectively. Our PSTUDA almost achieves the

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best average DSC results on all sequences. Compared to the first set of experiments, the performance of CycleGAN has decreased by about 6.8% and 4.4% on FS T2W and T2W, respectively, while the performance of the proposed PSTUDA has decreased by 0.5% and increased by 1.2% on these two sequences. These results underscore PSTUDA's exceptional robustness tasks with larger domain differences. As shown in Fig. in Appendix C our PSTUDA outperforms other comparative methods in maintaining structural consistency in translated images. Moreover, thanks to the one-to- one-to-multipl features, method also shows a significant advantage in training efficiency, which will be detailed in the subsequent section. In summary, the highlight of PSTUDA lies in its dual improvement of segmentation performance and training efficiency.
Table 2: Quantitative segmentation results of different comparative methods on the KiTS19 and MSKT datasets.
CT Tic
CT
FF T2W
CT
-T2W
CT .DWI
Metrics
Mothods
Kidney
Tumor
Avg
Kidney
Tummr
Avg.
Kidney
Tumar
Avg.
Kidney
Tumor
Avg
Supervised training
90.74
85.69
88.22
90.14
88.73
89.
87.53
80.68
84.11
901.0
84.76
87.48
W/o adapeation
71.20
13.27
42.24
43.75
6.27
25.01
9.13
22.51
15.72
49.25
4.13
26.69
1 CycleGAN 14
83.96
70.63
78.30
79.08
67.64
73.36
69.68
45.52
57.60
87.45
81.35
84.40
MUNIT 4]
76.71
67.21
71.96
72.36
66.70
69.53
72.55
47.52
60.01
75.37
55.32
65.35
DSC (%)
SIFA 35
76.34
48.12
62.23
71.69
56.56
61.13
49.39
23.25
36.32
72.31
31.11
$1.86
DHPPL 39
82.57
73.08
77.83
81.31
71.87
76.59
37.95
28.77
33.36
82,70
71.51
77.11
StarGAN v2
22
53.43
24.82
39.13
62.96
21.53
42.25
47.51
13.13
30.32
69.36
10.89
40.13
PSTUDAOUrs)
73.44
77.70
76.56
56.85
66.71
85,91
79.17
82.54
Supervised training
5.98
21.24
15.11
2.47
1.78
2.13
3:19
8.25
5.87
2.04
3.36
2.70
W/o adaptation
11.51
56.79
34.15
20.75
64.69
42.72
45,22
100
72.61
17.23
73.67
45.45
CycleGiN
6.57
32.86
19.72
5.23
4.12
6.33
29.38
17.85
5,48
2.78
4.13
HDu (mm)
MUNIT 41
7.66
53.06
30.36
4.96
16.94
10.95
6.40
32.34
19.37
4.43
19.12
11.78
A5
77.79
50.42
28.08
17.12
87.59
49.33
59.71
34.93
DEPL.
16.91
50.66
33.78
36.37
20.90
14,91
70.16
42.54
23.34
14.69
StarGAN
22
15.95
65.61
50.28
28.71
78.08
48.57
24.07
61.15
42.61
PSTUDA(Ours)
18.97
26.17
16.35
Training efficiency In this section, we evaluate the performance of PSTUDA in comparison with CycleGAN, MUNIT, and StarGAN in terms of model parameters and FL.OPs. As shown in Table Appendix the FLOPs for CycleGAN and MUNIT represent the cumulative results of the image translation tasks for the three domains, i.e., TIc-FS T2W, TIc+T2W, and TWII For the transfer of Tic to the other three MR sequences using CycleGAN, MUNIT, and StarGAN v2, the parameters that need to be optimized are 2, 3.3, and 1.9 times that of PSTUDA, respectively. Regarding FLOPs, PSTUDA demonstrates reduction of 72% and 79% compared to CycleGAN and MUNIT, respectively, but shows a 17% increase compared to StarGAN v2. The increased computational demand is mainly due to the PIN module in PSTUDA, which requires per-pixel fusion of style and content features. It is worth noting that the training efficiency of PSTUDA becomes more advantageous when more target domains are included in a one-to-multipe domain adaptation task. Overall, PSTUDA substantially decreases both model parameters and FLOPs while maintaining high segmentation accuracy, implying lower memory requirements and faster inference speed. In summary, these results highlight the significant advantages of PSTUDA in enhancing the efficiency and feasibility of clinical applications.
4.3 Ablation Study
Effectiveness of PST and MS D To investigate the effectiveness of the proposed Progressive Style Transfer (PST) paradigm, we conduct ablation studies on it with the Multi-Scale Discriminator (MS D) the MSKT dataset. As shown in Table the introduction of PST significantly improves the DSC and HDy5 metrics compared to the Baseline. This reflects the ability the PST to enhance the realism of translated images while maintaining structural consistency. Furthermore, the multi- scale discriminator assesses the pseudo-target domain images at different scales, thereby further enhancing the capabilities of the generator.
Effectiveness of M L_SD and PIN We also conduct ablation studies on the internal sub-modules within PST, namely the Multi-level Style Dictionary (M_SD) and the Point-wise Instance Normal- ization (PIN). As illustrated in Table replacing the Mapping Network and Style Encoder in the

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Baseline with the M__D results in a significant performance improvement. This can be attributed the stability and representativeness of the learned style encodings. Moreover, the integration of the M_MM L_SD with PIN fully considers the details and local style variations of images, thereby yielding exceptional results.
Table 3: Ablation study of Progressive Style Transfer paradigm (PST) and Multi-Scale Discriminator (MS D) on the MSKT dataset. The Baseline model is StarGAN v2.
Modules
TIc-FF T2W
T1c-T2W
TWc-DWI
Metrics
PST
MS_D
Kidney
Tumar
Avg.
Kidney
Tumor
Avg.
Kidney
Tumor
Avg.
74.96
58.46
66.71
58.48
17.05
37.77
69.29
41.41
55.35
DSC (%)
84.94
77.74
81.34
70.16
52.80
61.48
85.09
72.06
78.58
V
86.30
76.36
81.33
77.26
53.77
65.52
86,99
74.23
80.61
5.33
59.68
32.50
11.44
93.41
52.42
14.55
68.95
41.75
HDss (mm)
5.70
33.03
19.36
8,45
52.71
30.58
4.22
10.04
7.13
d
4.66
25.85
15.25
6.81
21.37
14.09
2.96
34.65
18.80
Table 4: Ablation study of Multi-level Style Dictionary (M SD) and Point-wise Instance Normaliza- tion (PIN) PST on the MSKT Dataset. The Baseline model StarGAN v2.
Modules
TIc-FS T2W
The T2w
The DD-
Metries
M SD
PIN
Kidney
Tumor
Avg.
Kidney
Tumor
Avg.
Kidney
Tumor
Avg.
74.96
58.46
66.71
58.48
17.05
37.77
69.29
41.41
55.35
DSC (%)
83.75
71.48
77.62
65.82
30.45
48.14
77.38
54.99
66.19
84.94
77.74
81.34
70.16
52,80
61.48
85.09
72.06
78.58
5.33
$9.68
32,50
11.44
93.41
52.42
14.55
68.95
41.75
HDas (mm)
4.89
40.75
22.82
8.94
53.27
31.11
4.50
59.67
32.08
5.70
33.03
19.36
8.45
52.71
30.58
4.22
10.04
7.13
Ablation of PIN and other normalization methods We compare PIN with AdaIN 12 and BIN [53], and as shown in Table PIN outperforms the others. attribute this superior performance to PIN's ability to account for local style differences, which particularly advantageous for fine- grained segmentation tasks (e.g., kidney tumors, as abnormal pathological tissues, exhibit significant style differences). This capability enables PSTUDA to generate synthetic images that align more closely with the data distribution of the target domain.
Table 5: Ablation study of PIN with AdaIN and BIN on the MSKT dataset.
Tic-FS T2W
Tic T2c
DWI
Normalization
Metrics
Kidncy
Tumor
Avg.
Kidney
Tumor
Avg.
Kidney
Tumor
Avg.
AdaIN
85.05
62.11
73.58
75.40
43.32
59.36
83.69
64.44
74.07
BIN
DSC
82.32
67.91
75.12
74.14
49.02
61.58
85.85
65.07
75.46
PIN
86.30
76.36
81.33
77.26
53.77
65.52
86.99
74.23
80.61
AdaIN
5.46
24.30
14.88
6.15
33.44
19.79
5.22
44.20
24.71
BIN
HDa5 (mm)
9.71
53.73
31.72
7.05
36.79
21.92
5.36
61.74
33.55
4.66
25.85
15.26
6.81
21.37
2.96
34.65
18.81
Conclusion
In this work, we propose novel and efficient One-to-Multiple PSTUDA framework that utilizes multi-level style dictionary decouple and store style information. By employing multiple cascaded style fusion modules, our framework progressively recombines content and style features, thereby achieving superior cross-modal alignment and consistency medical tissue structures. Experimental validation on both private dataset and public dataset demonstrates the significant advantages of our method in improving training efficiency for one-to-multiple domain adaptation tasks and enhancing the accuracy of multi-eqquence kidney tumor segmentation.
10

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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 62272404, 62076007, and 62372170, in part by the Natural Science Foundation of Hunan Province of China under Grants 2022JJ30571 and 2023JJ40638, and in part by the Research Foundation of Education Department of Hunan Province of China under Grant 23A0146.
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Appendix
In the appendix of this paper, Section [A provides a detailed introduction to the experimental datasets and evaluation metrics. Subsequently, Section [ offers the specific implementation details of the ex- periments. Section ppriients a visual comparison of the experimental results from different methods on the KiTS19 and MSKT datasets. Section D shows a comparison of training efficiency among different methods. Sections [ and I present two sets of supplementary comparative experiments and two sets of supplementary ablation experiments, respectively. Finally, Section G discusses the potential limitations and the broad impacts of our method.
A Details of the Experimental Datasets and Evaluation Metrics
MSKT The MSKT dataset contains 104 cases of sequine kidney and kidney tumor MRI data. Each case includes four MRI sequences: Tic, FS T2W, T2W, and DWI. The data are stored in NRRD format and scanned in axial view. The MR images have resolution of 512 x 512 x N, where N represents the number of slices, ranging from 11 to 96. The slice thickness varies from 2 to millimeters. Annotations were created by experienced clinicians, with normal kidney tissue regions labeled as I, kidney tumor regions as 2, and other areas labeled as O, considered as the background. In the first set of comparative experiments (Section 4.2), p, we randomly divide all cases into source domain training sets, target domain training sets, and target domain test sets in a 4.5:4.5:1 1 ratio. The annotated Tic data, comprising 46 cases, serve as the source domain, while the unannotated FS T2W, T2W, and DWI data, each comprising 46 cases, constitute the target domain. The remaining 12 cases are used as the test set. This division ensures that there is no intersection between the source domain, target domain, and test to prevent information leakage. The number of samples and slices included in each sequence is detailed in T
To protect patient privacy, all data were anonymized during collection and processing, removing any information that could identify the patients. The use this dataset has been reviewed and approved by the Institutional Review Board (IRB) to ensure that the research complies with ethical standards and safeguards participant rights. We strictly adhere to relevant laws and regulations regarding data use and protection to ensure the legal and compliant use of the data.
KiTS19 The KiTS19 dataset, established for the 2019 kidney and kidney tumor segmentation challenge, comprises multi-phass CT scans along with corresponding segmentation annotations for 300 patients. A total of 210 cases are designated for model training, and the remaining 90 are used evaluate model performance. The CT images have a resolution of 512 x 512 N, where N ranges from 29 to 1,059 slices. For our second set of comparative experiments (Section 4.2). we combine the KiTS19 19 dataset with the MSKT dataset. Specifically, provides only the training set, we use all cases from this set as our source domain training set. which includes 45,424 CT slices, with 16,336 slices containing targets. All four sequences of the MSKT are considered as the target domain, and we merge the 92 cases identified as source and target domains in Table 6 to form the target domain training set, while the test set remains unchanged. Please note that in all of our experiments, the slices used for training all contain the kidney or tumor targets, while the slices used for testing are all the slices in each case.
the KiTS19 challenge initially
Evaluation metrics Following 21 the Dice Similarity Coefficient (DSC) and the 95% Hausdorff Distance (HDgs) are utilized for quantitative comparisons. It is important to note that if the predicted results and the true annotations do not contain the same categories, directly calculating HD may lead to errors. To address this issue, we set the HDys value to 100 in cases of potential calculation errors, indicating no overlap between the predicted results and the true annotations.
B Implementation Details
All experiments are conducted on an NVIDIA A800 GPU utilizing the PyTorch framework. CT and MR data are uniformly processed to a resolution of 256x 256. In the one-to-multiple domain adaptation stage, the number of domains is set to and 5 for the two sets of experiments, respectively (including source and target domains). The length of the multi-level style dictionary is set to 4,096. The number of style fusion modules in the generator is 4. The weights of adversarial loss, cycle
16

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Table 6: Data partitioning for each sequence in the MSKT dataset.
Source
Target
Test
Sequence
cases
slices
slices with target
cases
slices
slices with target
cases
slices
slices with target
Tlc
46
3444
2196
FS T2W
1110
T2W
46
1097
754
12
288
204
DW1
46 1102
783
12
288
213
consistency loss, and identity loss are set to 1, 10, and 1, respectively. Network optimization employs the Adam optimizer [6]] with an initial learning rate of le-4, and the batch size is 8. Other hyperparameters follow the configuration StarGAN v2. the segmentation stage, U-Net is chosen as the network architecture, paired with the Adam optimizer starting with learning rate of le-4. The batch size is set to 16, with a total training duration of 50 epochs. In the above setup, it takes about 18 hours for our method to fully execute once.
B.1 Network Architecture
Generator As shown in Table 7 the generator within PSTUDA comprises encoder, four style fusion modules, and a decoder. The encoder includes three sets of convolutional blocks and four Resnet blocks, while the decoder is composed of upsampling layers with transposed convolution operations and one convolutional block. Instance Normalization is applied during the encoding phase, and Point-wise Instance Normalization utilized within the style fusion modules and the decoder.
Table 7: Architecture of the generator.
Layer
Stride
Padding
Norm
Repeat
Output shape
Input
256x256x1
Encoder: Conv7x7
1
0
IN
1
256x256 64
Encoder: Conv3x3
2
IN
128x 128x x 128
Encoder: x3
2
1
IN
1
64x64x256
Encoder: Resnet Block
1
0
IN
64x64x256 4
Style Fusion Module
0
PIN
64x64x256
Decoder: upsample
2
1
PIN
128x 128x 128
Decoder: upsample
2
1
PIN
1
256x256
Decoder: Conv7x7
1
0
1
256x256x1
Discrininator As shown in Fig. 2 in Section 3.4 utilize four independent and identical
discrimination branches in the multi-scale discriminator. The detailed architecture of the first discrimination branch, which takes the original size image input, is presented in Table g
Table 8: Architecture of the discriminator.
Layer
Stride
Padding
Repeat
Output shape
Input x
256x256x
Conv3x3
256x256x64
ResBlk
1
1
128 128
ResBlk
1
I
64x64x256
ResBlk
1
I
32x32x512
ResBlk
1
1
16x 16x512
Conv x
I
0
16x 16x
Conv 1x
1
0
16x16x4
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B.2 Implementation of Comparative Methods
This method is derived from the GitHub repository: CycleGAN-ame pixx In our experiments, ResNet 9block and PatchGAN with 3 convolutional layers are selected as the generator and discriminator for CycleGAN, respectively. The weights for adversarial loss (MSE), cycle consistency loss (I.1), and domain- invaaiinn perceptual loss (L.1) are set I, 10, and 5, respectively. Other hyperparameters are consistent with the open-source code. During the segmentation phase, all hyperparameters for U-Net are the same as those in our PSTUDA.
CycleGAN
The architecture of the MUNIT network remains the same the open-source code. The weights for adversarial loss (MSE), image reconstruction loss (L.I,, style reconstruction loss (L.I), and content reconstruction loss (L.1) are set to 1, 10, 1, and 1, respectively. In the segmentation stage, all U-Net hyperparameters are identical to those in our PSTUDA
MUNIT
This method is from the GitHub repository: https//ggiubb..o/////aaa//////
SIFA This method is available from the GitHub repository: pytorch The official version of this method is implemented in TensorFlow, available at In OMUDA, they have re-implemented SIFA in PyTorch, and we have used the Py Torch version of SIFA to ensure all methods use the same framework for fair comparison. All settings, hyperparameters, and losses used in our experiments the same those in the open-source code.
DEPL This method is based on own implementation.
StarGAN v2 This method from the GitHub repository: In our experiments, the network architecture is consistent with the official code. The weights for RI regression loss, adversarial loss (CE), cycle consistency loss (LI), style reconstruction loss (L1), and diversity-sensitiv loss (L1) are all set to 1. High-pass filtering is not used in this comparative experiment. During the segmentation phase, all U-Net hyperparameters are the same as those in PSTUDA.
C Visualization of the KiTS19 and MSKT Experiments
In this section, we present the visualized results of the second set of comparative experiments conducted on the KiT19 and MSKT datasets, and these visualizations are discussed in Section 4.2
Source CT
CyclGANN
MUNIT
SIFA
DEPL
StarGAN v2
PSTUDA
CT
4 Tzw
$
T2W
CT $ DWI
Figure 4: Qualitative results for CT > Tlc, FS T2W, T2W, and DWI on the KiTS19 and MSKT datasets.
18

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D Training Efficiency
This section displays the comparative outcomes terms of model parameters and FLOPs among different cross-domain image translation methods. As illustrated in Table we compare PSTUDA with CycleGAN, MUNIT, and StarGAN v2, because these methods do not involve segmentation stage. It is worth note that the style dictionary itself does not contribute the computation of FLOPs. All models are trained using FP32 precision. The detailed analysis of these comparative results is provided in Section 4.2
Table 9: Model parameters and FLOPs of different methods.
Methods
Components
Parameters
FLOPs
Ga
11.37M x
112.08G x 3
Gø
11.37M x 3
112.08G x 3
CycleGAN
DA
2.76M 3
6.24G 3
DB
2.76M x 3
6.24G x 3
Total
28.26M x 3
236.64G x
GA
15.01M x 3
152.18G x 3
Gg
15.01M x 3
152.18G x 3
MUNIT
DA
8.27M 3
4.28G 3
DB
8.27M x 3
4.28G x 3
Total
46.56M x 3
312.92G x
G
33.89M
97.30G
Nи
4.08M
0.01G
StarGAN v2
Es
20.98M
32.98G
D
20.85M
32.98G
Total
79.80M
163.27G
G
12.17M
154.84G
D
29.92M
41.46G
PSTUDA
Ds
0. 1.1M
Total
42.19M
196.30G
G: Generator, D: Discriminator. Ny: Mapping Network. Ey: Style Encoder, Dy:
Stylc Dictionary.
E
Supplementary Comparative Experiments
To further validate the generalization capability of PSTUDA, we conduct bidirectional cross-modal validation experiments on publicly available abdominal multi-organ dataset [22 and perform reverse validation experiments from MR to CT on the MSKT and KiTS19 datasets. The results, as shown in Tables 10 and II] indicate that our method significantly outperforms StarGAN v2 (baseline) both experimental groups.
F
Supplementary Ablation Experiments
The multi-level style dictionary and style fusion module are the core components of PSTUDA, and these two modules include the following hyperparameters: the depth of the style dictionary, the number of levels in the style dictionary, and the number of style fusion modules. It is important to note that number of levels the style dictionary equal the number style fusion modules. To investigate the sensitivity of model performance to various hyperparameters, we conduct extensive ablation studies on depth of the multi-level style dictionary (M_SD) and the number of style fusion modules (SFM). As presented in Tables and
B under the original settings (dictionary
19

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Table 10: Quantitative segmentation results for bidirectional cross cross-modal experiments on the abdomi- nal multi- organ dataset.
Metrics
Methods
CT-MR
MR-CT
Liver
kidney
L. kidney
Spleen
Avg.
Liver
R. kidney
L. kidney
Spleen
Avg.
Supervised training
94,39
90.86
73.38
78.00
84.16
87.45
69.33
77.76
75.61
77.54
adaptation
23.44
1.99
12.77
20.28
14.62
37.33
0.00
0.26
1.29
9.72
(S))
StaGAN
30.93
PSTUDA
88.15
74.20
70.51
71.99
76.21
53.54
53.42
60.28
38,48
51.43
Supervised training
2.04
51.59
5.86
84.09
35.89
14.72
15.68
38.17
54.15
30.68
Wlo adaptation
37,91
75.82
62.57
75.80
63.03
33.22
80.59
49.00
65.36
57.04
HD (mm)
StuGAA v2
45.12
57.08
61.45
115.49
69.78
33,70
71.15
75.29
100.16
70.08
70.11
43.02
Table 11: Quantitative segmentation results from MR CT on the MSKT and KiTS19 datasets.
MR(TIc)- CT
MR(FS CT
Metrics
Methods
Kidney
Tumor
Avg.
Kidney
Tumor
Avg.
Supervised training
91.98
66.61
79.30
91.98
66.61
79.30
W/o adaptation
41.87
16.26
29.07
8.07
11.36
9.72
DSC (%)
StarGAN v2
34.44
24.02
29.23
46.17
22.96
34.57
PSTUDA
73.13
56.23
64.68
78.13
50.85
64.49
Supervised training
33.66
45.89
39.78
33.66
45.89
39.78
W/o adaptation
64.91
100.61
82.76
57.90
102.54
80.22
HDgs (mm)
StarGAN v2
69.72
96.61
83.17
57.99
92.36
75.17
PSTUDA
56.95
93.65
75.30
40.82
50.25
45.54
Table 12: Ablation study on the depth of the Multi-level Style Dictionary (M SD) on the MSKT dataset.
TIc-FF T2W
Tlc+T2W
TWc-DWI
M. SD Depth
Metries
Kidney
Tumor
Avg.
Kidney
Tumor
Avg.
Kidney
Tumor
Avg.
256
84.85
66.34
75.60
70.55
43.85
57.20
83.15
63.52
73.34
1024
85.84
69.68
77.76
70.06
42.76
56.41
83.99
64.41
74.20
DSC (%)
4096
86.30
76.36
81.33
77.26
53.77
65.52
86.99
74.23
80.61
16384
84.63
66.74
75.69
77.63
57.49
67.56
83.31
68.78
76.05
Table 13: Ablation study the number of Style Fusion Modules (SFM) on the MSKT dataset.
Tic T2W
T1c-T2WW
DWW
SFM Number
Metrics
Kidney
Tumor
Avg.
Kidney
Tumor
Avg.
Kidney
Tumor
Avg.
85.65
71.77
78.71
73.94
44.51
59.23
83.89
61.31
72.60
86.30
76.36
81.33
77.26
53.77
65.52
86.99
74.23
80.61
DSC (%)
84.49
67.94
76.22
74.97
50.57
62.77
81.20
60.44
70.82
8
84.09
63.86
73.98
60.06
30.43
45.25
83.75
60.60
72.18
depth 4,096 and module number of 4), PSTUDA performs optimally in most experiments. Notably, the segmentation performance on the T2W sequence improves when the dictionary depth is increased to 16,384.
G Limitations and Broader Impacts
Limitations Our work presents a domain adaptation method for multi-target domain image transla- tion. However, the field of onc-to-multiple domain adaptation is not extensively explored, and our
20

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study may have some limitations that should be acknowledged. Firstly, the tasks we focus on are confined domain adaptation of multi-sequeence MR images and the segmentation of kidneys and tumors presented in this work. In future research, we aim to broaden our scope include domain adaptation for other medical and natural images, as well as other downstream tasks such as object detection. Secondly, compared to the ideal one-to-multiple model, our current model inherently operates within a broader multi-domain to multi-domain adaptation framework. Therefore, in our future studies, aspire to design more specialized one-to-multiple model, which is expected to further improve model performance and reduce model complexity.
Broader impacts In fact, the impact of one-to to--tutimllple domain adaptation technology is profound, especially when facing numerous target domains and limited annotated data. Our PSTUDA framework can efficiently extend an annotated source domain to adapt to multiple unannotated new domains, significantly reducing the time and resource investment required for multi-domain transfer tasks. This capability is crucial in complex medical data environments, as it not only greatly alleviates the workload of medical professionals but also holds promise for improving the diagnosis and treatment processes for patients. Overall, our research enhances the flexibility and efficiency of domain adaptation technology, which advances the field of machine learning and brings innovative solutions critical industries such healthcare.
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Reviews:
[{"review_id": "AnAk4qSFjh", "reviewer": "Reviewer_yJwR", "summary": "The paper presents a one-to-multiple progressive style transfer unsupervised domain adaptation framework designed for kidney and tumor segmentation. \n\nIt aims to mitigate the challenges of annotation burden and domain differences by employing a multi-level style dictionary and cascading style fusion modules. \n\nIt demonstrates significant improvements in segmentation performance and efficiency on the MSKT and KiTS19 datasets.", "questions": "Please see my comments above.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "x. The introduction of a multi-level style dictionary and point-wise instance normalization (PIN) for progressive style transfer that effectively decouples content and style features.\n\nx. It significantly reduces the floating-point computation by approximately 72% and the number of model parameters by about 50%, highlighting its efficiency and feasibility for practical clinical applications.", "weaknesses": "x. **Concern about novelty.**\n\nFirst, the authors extended the UDA setting to 'one-to-multiple'. I am not sure about its practical or clinical relevance here compared to continued UDA or UDA on evolving domains. \nSecond, there seems to be limited novelty compared to the architecture of OMUDA derived from StarGAN v2. \n\nx. **Concern about the effectiveness of PIN**: \nPoint-wise Instance Normalization is a central component of the framework, but its effectiveness compared to other normalization techniques is not thoroughly evaluated. There might be scenarios where the PIN is less effective, and alternative normalization methods like Adaptive Instance Normalization or Batch-Instance Normalization might perform better. \n\nx. **Concern on analysis.**\nThe multi-level style dictionary and the cascading style fusion modules. Understanding the contribution of each part would help in refining the model. The sensitivity of the model’s performance to various hyperparameters, such as the temperature parameter (\\tao), the number of style fusion modules, and the depth of the style dictionary, is not extensively studied.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your constructive and insightful comments. We address all weaknesses below.\n\n**W1. Practical and clinical relevance of PSTUDA.** \n\nPSTUDA is designed as a one-to-multiple UDA framework based on multi-sequence MRI segmentation tasks, thus having strong clinical relevance. Compared to methods that train target domains sequentially (including continued UDA and UDA on evolving domains), PSTUDA offers the following advantages:\n\n* **Flexibility:** Sequential methods treat all target domains as a single domain during training, which prevents them from generating images of the specified target domain during inference, potentially leading to inaccurate results. In contrast, PSTUDA can generate images of the specified target domain as needed during inference. This flexibility meets the diverse requirements of clinical applications.\n* **Training efficiency:** Sequential methods train in a serial manner, whereas PSTUDA trains all source and target domains in parallel, significantly reducing training time. Additionally, PSTUDA’s generator and multi-level style dictionary enable quick deployment and fast inference, meeting clinical demands.\n\n**W1. Novelty of PSTUDA.** \n\nPSTUDA is inspired by OMUDA and StarGAN v2. However, we believe there are some fundamental differences between our method and these models:\n\n* **Style feature acquisition:** OMUDA extracts style features from a style encoder or mapping network, which lacks representativeness and stability. In contrast, PSTUDA addresses this issue by using a multi-level style dictionary to directly learn the global style features of each domain.\n* **Generator architecture:** Both OMUDA and PSTUDA use the CycleGAN generator. However, OMUDA uses the same style features in each layer of AdaIN, while PSTUDA replaces AdaIN with PIN and progressively injects different style features layer by layer to achieve image translation.\n* **Discriminator architecture:** OMUDA uses the StarGAN v2 discriminator, whereas PSTUDA employs a multi-scale discriminator to better leverage the capabilities of its powerful generator integrated with PIN.\n\nThe ablation experiments in Appendix E demonstrate the effectiveness of our method.\n\n**W2. Evaluation of PIN effectiveness.**\n\n AdaIN [1] and BIN [2] provide global scaling parameters for each channel of the content feature map. In contrast, our PIN offers unique scaling parameters (mean and standard deviation) for each local spatial point in each channel.\n\nWe believe that PIN is particularly valuable for fine-grained segmentation tasks due to its ability to consider local style differences, enabling PSTUDA to generate synthetic images that better match the target domain data distribution.\n\nTo demonstrate our perspective, we conducted ablation experiments on the three methods, as presented in the table below. The results indicate that PIN outperforms AdaIN and BIN in terms of performance, especially in kidney tumor segmentation. This superiority can be attributed to the fact that kidney tumors, being abnormal pathological tissues, exhibit significant style differences which can be finely processed by PIN.\n\n| **Normalization** |           | **T1c→FS T2W** |           |           | **T1c→T2W** |           |           | **T1c→DWI** |           |\n| :---------------: | :-------: | :------------: | :-------: | :-------: | :---------: | :-------: | :-------: | :---------: | :-------: |\n|                   |  Kidney   |     Tumor      |   Avg.    |  Kidney   |    Tumor    |   Avg.    |  Kidney   |    Tumor    |   Avg.    |\n|       AdaIN       |   85.05   |     62.11      |   73.58   |   75.40   |    43.32    |   59.36   |   83.69   |    64.44    |   74.07   |\n|        BIN        |   82.32   |     67.91      |   75.12   |   74.14   |    49.02    |   61.58   |   85.85   |    65.07    |   75.46   |\n|        PIN        | **86.30** |   **76.36**    | **81.33** | **77.26** |  **53.77**  | **65.52** | **86.99** |  **74.23**  | **80.61** |\n\n**Additional comment:** We would have liked to include the rest of answers to the questions mentioned by Reviewer **yJwR** (marked as **W3**). Unfortunately, we did not have enough space in this rebuttal box. As soon as the discussion phase will begin, we will include the mentioned answers in an additional comment for the reviewer.", "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": "4WKpPL4Igs", "reviewer": "Reviewer_tgyp", "summary": "The authors propose a novel and efficient One-to-Multiple Progressive Style Transfer Unsupervised Domain-Adaptive (PSTUDA) framework to address the UDA task for MRI sequences. Specifically, they developed a multi-level style dictionary that explicitly stores style information for each target domain at different stages, reducing the burden on a single generator in multi-target transfer tasks and effectively decoupling content and style. Additionally, multiple cascaded style fusion modules are employed, utilizing point-wise instance normalization to progressively recombine content and style features, thereby enhancing cross-modal alignment and structural consistency. Experiments conducted on both the private MSKT and public KiTS19 datasets demonstrate the effectiveness of this approach.", "questions": "1.\tReading content from the multi-level style dictionary, does it incur additional overhead? If a new unknown domain is encountered, the learning of the multi-level style dictionary requires a warm-up (there is additional overhead the first time a new domain is encountered).\n2.\tWhat is puzzling is that Table 6 shows the multi-level style dictionary only takes up 0.1M; does this include tensors?\n3.\tIs there any difference between the Generator and Discriminator Architecture in Section 3.4 and previous work?\n4.\tThe theory of achieving independent style transfer for each pixel using Point-wise Instance Normalization (PIN) requires further validation. While it may be effective, the explanation of its application in style transfer is not sufficiently thorough. Additionally, although the targets in medical image segmentation are more precise, this does not necessarily imply that style adjustments need to be more precise. Further explanation is needed.", "limitations": "The authors have addressed the limitations of the current work.", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.\tA multi-level style dictionary was developed, which reduces the burden on a single generator in multi-target transfer tasks and effectively decouples content and style.\n2.\tMultiple cascaded style fusion modules are employed to progressively recombine content and style features, thereby enhancing cross-modal alignment and structural consistency.\n3.\tThe organization of the paper's structure is excellent.", "weaknesses": "1.\tThe open-source code is not explicitly provided.\n2.\tThe explanation of the One-to-Multiple Framework is somewhat rough and needs more details.\n3.\tPSTUDA needs to be validated bidirectionally on cross-modal data, such as MRI and CT.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your constructive and insightful comments. We address all weaknesses and questions below.\n\n**W1. Open-source code is not explicitly provided.**\n\nWe will release the code, pre-trained models, and detailed implementation guidelines upon acceptance. To ensure reproducibility, we build on the open-source implementations of StarGAN v2 and CycleGAN, and provide all relevant hyperparameters in Appendix B.\n\n**W2. One-to-Multiple framework needs more details.**\n\nWe apologize for this and will provide a more detailed description of our One-to-Multiple PSTUDA framework in subsequent versions.\n\n**W3. Bidirectional validation on cross-modal MRI and CT data.**\n\nAccording to your suggestion, we conducted bidirectional cross-modal validation on the abdominal multi-organ dataset [1-2] and performed reverse validation experiments from MR to CT on the MSKT and KiT19 datasets. The results are shown in the following two tables. Both sets of experiments demonstrate that our method significantly outperforms StarGAN v2 (baseline).\n\n|     **Methods**     |           |           | **CT→MR** |           |           |           |           | **MR→CT** |           |           |\n| :-----------------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: |\n|                     |   Liver   | R. kidney | L. kidney |  Spleen   |   Avg.    |   Liver   | R. kidney | L. kidney |  Spleen   |   Avg.    |\n| Supervised training |   94.39   |   90.86   |   73.38   |   78.00   |   84.16   |   87.45   |   69.33   |   77.76   |   75.61   |   77.54   |\n|   W/o adaptation    |   23.44   |   1.99    |   12.77   |   20.28   |   14.62   |   37.33   |   0.00    |   0.26    |   1.29    |   9.72    |\n|     StarGAN v2      |   75.04   |   68.16   |   62.83   |   68.13   |   68.54   |   44.32   |   28.05   |   26.51   |   24.85   |   30.93   |\n|       PSTUDA        | **88.15** | **74.20** | **70.51** | **71.99** | **76.21** | **53.54** | **53.42** | **60.28** | **38.48** | **51.43** |\n\n|     **Methods**     |           | **MR(T1c)→CT** |           |           | **MR(FS T2W)→CT** |           |\n| :-----------------: | :-------: | :------------: | :-------: | :-------: | :---------------: | :-------: |\n|                     |  Kidney   |     Tumor      |   Avg.    |  Kidney   |       Tumor       |   Avg.    |\n| Supervised training |   91.98   |     66.61      |   79.30   |   91.98   |       66.61       |   79.30   |\n|   W/o adaptation    |   41.87   |     16.26      |   29.07   |   8.07    |       11.36       |   9.72    |\n|     StarGAN v2      |   34.44   |     24.02      |   29.23   |   46.17   |       22.96       |   34.57   |\n|       PSTUDA        | **73.13** |   **56.23**    | **64.68** | **78.13** |     **50.85**     | **64.49** |\n\n**Q4. Theory of PIN.**\n\nWe believe that PIN has significant advantages in certain complex scenarios. For example, in kidney tumor medical imaging data, the contrast, texture, and morphology of the lesion areas exhibit significant differences from normal tissues. These differences ultimately stem from variations in data distribution. In such cases, PIN customizes a set of scaling parameters for each local spatial point in the content feature map for each channel, allowing for more precise alignment of local differences, thereby making the translated images closer to the real target domain’s data distribution. The calculation formula for PIN is:\n$$\n\\huge y_{nchw} = \\gamma_{nchw}(v_{ss}) \\cdot \\hat x_{nchw} + \\beta_{nchw}(v_{ss})\n$$\n$$\n\\huge \\hat x_{nchw} = \\frac{x_{nchw} - \\mu_{nc}}{\\sqrt{\\sigma_{nc}^2 + \\epsilon}}\n$$\n$$\n\\huge \\gamma_{nchw}(v_{ss}), \\beta_{nchw}(v_{ss}) = chunk(h_{n(2c)hw})\n$$\n$$\n\\huge h_{n(2c)hw} = ConvBlock(v_{ss}) \n$$\nwhere $\\gamma$ and $\\beta$ are obtained through convolutional transformations via $v_{ss}$, and their dimensions are consistent with those of $\\hat x_{nchw}$.\n\n**Q4. Effectiveness and Interpretation of PIN.**\n\nWe conducted a comparison of PIN with the widely used AdaIN [3] and BIN [4], as shown in the table below. The results show that our PIN achieves the best performance. Theoretically, fully supervised segmentation results should be the upper limit for UDA, as it is conducted within the same domain, with both the training and test sets belonging to the same distribution. The key challenge in UDA tasks lies in addressing the differences in data distribution between different domains. Hence, the closer the distribution of the translated images is to the real target domain’s data distribution, the better the segmentation performance. Thus, we believe that the precise style adjustments in PIN will further reduce data distribution differences, leading to more accurate target segmentation.\n\n| **Normalization** |  | **T1c→FS T2W** |  |  | **T1c→T2W** |  |  | **T1c→DWI** |  |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|  | Kidney | Tumor | Avg. | Kidney | Tumor | Avg. | Kidney | Tumor | Avg.  |\n| AdaIN | 85.05  | 62.11  | 73.58  | 75.40  | 43.32  | 59.36  | 83.69  | 64.44  | 74.07   |\n| BIN | 82.32  | 67.91  | 75.12  | 74.14  | 49.02  | 61.58  | 85.85  | 65.07  | 75.46   |\n| PIN | **86.30** | **76.36** | **81.33** | **77.26** | **53.77** | **65.52** | **86.99** | **74.23** | **80.61** |\n\n**Additional comment:** We would have liked to include the rest of answers to the questions mentioned by Reviewer **tgyp** (marked as **Q1**,**Q2**, and **Q3**). Unfortunately, we did not have enough space in this rebuttal box. As soon as the discussion phase will begin, we will include the mentioned answers in an additional comment for the reviewer.", "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": "FR0joPJFOi", "reviewer": "Reviewer_pquR", "summary": "The paper proposes the One-to-Multiple Progressive Style Transfer Unsupervised Domain-Adaptive (PSTUDA) framework for kidney and tumor segmentation in multi-sequence MRI, addressing inefficiencies in existing one-to-one UDA methods. PSTUDA features a multi-level style dictionary and multiple cascading style fusion modules using point-wise instance normalization to recombine content and style features progressively. Tested on private (MSKT) and public (KiTS19) datasets, PSTUDA showed 1.8% and 3.9% improvements in Dice Similarity Coefficient for kidney and tumor segmentation, respectively, and achieved significant reductions in model parameters and computational costs. The framework outperformed state-of-the-art UDA methods, enhancing both segmentation performance and training efficiency.", "questions": "- I would agree with the concept of the multiple cascading modules and Point-wise Instance Normalization but am surprised it doesn't benefit the overall segmentation results further. Can the authors provide their insights into why there isn't a more dramatic boost? What future work do they envision to achieve more substantial performance boosts?\n\n-  paper demonstrates the effectiveness of PSTUDA on kidney and tumor segmentation in multi-sequence MRI. Have the authors tested the framework on other medical imaging tasks or modalities, such as CT or ultrasound? What would expected results and challenges be in other modalities?\n\n- Despite the reduction in model parameters and FLOPs, PSTUDA still appears resource-intensive. How feasible is the deployment of PSTUDA in real-world clinical settings with limited computational infrastructure?\n\n- Are there any plans to release code, pre-trained models, or detailed implementation guides? How can the research community best leverage PSTUDA in their own work?", "limitations": "Limitations and Broader Impacts are appropriately addressed.", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper demonstrates several strengths across various dimensions. \n\nIn terms of originality, PSTUDA introduces a relatively interesting approach by employing cascaded style fusion modules and Point-wise Instance Normalization (PIN) to achieve excellent cross-modal alignment and structural consistency. This combo allows for more precise and progressive recombination of content and style features, addressing limitations in existing one-to-one UDA methods and extending the applicability of domain adaptation techniques in medical imaging.\n\nThe strong review of related works situates PSTUDA within the broader context of domain adaptation and medical image segmentation.\n\nThe paper is well-written and clear, providing detailed explanations of the architecture and methodologies. It emphasizes structural consistency, which is critical for medical image analysis. The comprehensive writing extends to the appendices, offering an appropriate level of detail on datasets, which aids in reproducibility and understanding\n\nPSTUDA’s experimental design is robust, featuring both comparative and ablation studies as well as appropriate metrics and analysis. \n\nThis paper is original, detailed, clear, and demonstrates reasonable improvements in segmentation with significant improvements in floating point operations and model size.", "weaknesses": "- The reported improvements in Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD95), while notable, are relatively modest (1.8% and 3.9%, respectively). \n\n- The framework's scalability to handle a larger number of target domains in real-world scenarios remains untested.\n\n- The generalizability of this approach beyond simple kidney/tumour segmentation into multi-class intra-organ segmentation or even multi-organ segmentation remains to be seen.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We would like to thank you for the positive consideration and the useful feedback on our work. We address all your concerns below.\n\n**W1. About Performance Enhancement.**\n\nThe one-to-multiple UDA task presents many additional challenges compared to the one-to-one task. For example, the differences between various target domains are distinct, and a single generator must coordinate the complex mapping relationships among multiple target domains, thereby making the learning process more difficult. However, a single generator can translate one source domain into multiple target domains, significantly reducing the training time and resource costs compared to one-to-one methods. Overall, our findings have shown significant advantages and we believe our approach holds substantial benefits for multi-sequence UDA tasks.\n\n**W2. Scalability to more target domains.**\n\nAs you suggested, we have acquired a new set of T1 MRI sequences from our partner hospital to validate the performance of our method in a one-to-five domain adaptive segmentation task. As shown in the table below, our method maintains high performance even with an increased number of target domains, which are derived from real-world clinical scenarios.\n|     **Methods**     |           | **CT→T1c** |           |           | **T1c→FS T2W** |           |           | **CT→T2W** |           |           | **CT→DWI** |           |           | **CT→T1** |           |\n| :-----------------: | :-------: | :--------: | :-------: | :-------: | :------------: | :-------: | :-------: | :--------: | :-------: | :-------: | :--------: | :-------: | :-------: | :-------: | :-------: |\n|                     |  Kidney   |   Tumor    |   Avg.    |  Kidney   |     Tumor      |   Avg.    |  Kidney   |   Tumor    |   Avg.    |  Kidney   |   Tumor    |   Avg.    |  Kidney   |   Tumor   |   Avg.    |\n| Supervised training |   90.74   |   85.69    |   88.22   |   90.14   |     88.73      |   89.44   |   87.53   |   80.68    |   84.11   |   90.20   |   84.76    |   87.48   |   84.40   |   67.42   |   75.91   |\n|   W/o adaptation    |   71.20   |   13.27    |   42.24   |   43.75   |      6.27      |   25.01   |   9.13    |   22.31    |   15.72   |   49.25   |    4.13    |   26.69   |   14.72   |   5.33    |   10.03   |\n|     StarGAN v2      |   51.87   |   25.06    |   38.47   |   54.18   |     21.94      |   38.06   |   42.10   |    9.08    |   25.59   |   56.17   |   15.14    |   35.66   |   59.38   |   20.32   |   39.85   |\n|       PSTUDA        | **83.88** | **73.88**  | **78.88** | **82.89** |   **77.08**    | **79.99** | **75.84** | **59.73**  | **67.79** | **84.37** | **72.15**  | **78.26** | **82.28** | **68.58** | **75.43** |\n\n**W3 and Q2. Abdominal multi-organ segmentation.**\n\nDue to the limited availability of medical data, we apologize that we were unable to collect a multi-target domain abdominal multi-organ dataset within a short timeframe. However, we collected a multi-organ dataset [1-2] consisting of CT and MR images from internet to validate the proposed method. The experimental results are presented in the table below.\n|     **Methods**     |           |           | **CT→MR** |           |           |           |           | **MR→CT** |           |           |\n| :-----------------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: |\n|                     |   Liver   | R. kidney | L. kidney |  Spleen   |   Avg.    |   Liver   | R. kidney | L. kidney |  Spleen   |   Avg.    |\n| Supervised training |   94.39   |   90.86   |   73.38   |   78.00   |   84.16   |   87.45   |   69.33   |   77.76   |   75.61   |   77.54   |\n|   W/o adaptation    |   23.44   |   1.99    |   12.77   |   20.28   |   14.62   |   37.33   |   0.00    |   0.26    |   1.29    |   9.72    |\n|     StarGAN v2      |   75.04   |   68.16   |   62.83   |   68.13   |   68.54   |   44.32   |   28.05   |   26.51   |   24.85   |   30.93   |\n|       PSTUDA        | **88.15** | **74.20** | **70.51** | **71.99** | **76.21** | **53.54** | **53.42** | **60.28** | **38.48** | **51.43** |\n\nWe observe that although the results of MR to CT showed an improvement of approximately 42% compared to no domain adaptation, there is still significant potential for enhancement in comparison to fully supervised methods. Furthermore, the results from CT to MR were better. We attribute this difference to the higher image quality of CT in contrast to MR, which presents a greater challenge for domain adaptation from MR to CT.\n\n**Q1 Insights and future work.**\n\nAs shown in Table 8 of Appendix E, the ablation study indicates that PIN brings significant performance improvements. However, the results for the multi-scale discriminator in Table 7 show that the improvement is not as pronounced. In our future work, we will conduct further investigation into different versions of discriminators. Furthermore, PSTUDA is essentially a multi-domain to multi-domain translation framework. We will also focus on developing a dedicated one-to-multiple UDA framework to achieve more substantial performance improvements.\n\n**Q3 Feasibility of deployment.**\n\nBy utilizing the generator and multi-level style dictionary from PSTUDA in a clinical environment, we can achieve rapid one-to-multiple domain adaptation. We conducted simulations of PSTUDA’s performance using minimal computational resources with clinical data from our partner hospitals. Our PSTUDA requires approximately 13M of storage and 15G of computational resources. Thus, we believe that PSTUDA can be easily deployed even in clinical settings with limited computational infrastructure.\n \n**Q4 Code open source.**\n\nWe will release the code, pre-trained models, and detailed implementation guidelines upon acceptance. \n\n[1] Kavur, A. E., et al. CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation. MedIA 2021.\n\n[2] Landman, B., et al. Multi-Atlas Labeling Beyond the Cranial Vault. 2017.", "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 and AC,

We sincerely appreciate your valuable time and effort spent reviewing our manuscript. We would like to thank all the reviewers for providing insightful comments and valuable suggestions. The valuable feedback from the reviewers has significantly contributed to enhancing the quality of our manuscript.

Based on the comments from the reviewers, we have summarized the strengths of our paper as follows:

* **Method: [Reviewer yJwR, tgyp, pquR]** PSTUDA introduces a relatively interesting approach by employing cascaded style fusion modules and Point-wise Instance Normalization (PIN) to achieve excellent cross-modal alignment and structural consistency. The introduction of a multi-level style dictionary and point-wise instance normalization (PIN) for progressive style transfer that effectively decouples content and style features. A multi-level style dictionary was developed, which reduces the burden on a single generator in multi-target transfer tasks and effectively decouples content and style. Multiple cascaded style fusion modules are employed to progressively recombine content and style features, thereby enhancing cross-modal alignment and structural consistency.

* **Experiment: [Reviewer yJwR, pquR]** It significantly reduces the floating-point computation by approximately 72% and the number of model parameters by about 50%, highlighting its efficiency and feasibility for practical clinical applications. PSTUDA’s experimental design is robust, featuring both comparative and ablation studies as well as appropriate metrics and analysis. This paper is original, detailed, clear, and demonstrates reasonable improvements in segmentation with significant improvements in floating point operations and model size.

* **Expression: [Reviewer tgyp, pquR]** The organization of the paper's structure is excellent. The paper is well-written and clear, providing detailed explanations of the architecture and methodologies. The comprehensive writing extends to the appendices, offering an appropriate level of detail on datasets, which aids in reproducibility and understanding.

* **Impact: [Reviewer pquR]** It emphasizes structural consistency, which is critical for medical image analysis. The strong review of related works situates PSTUDA within the broader context of domain adaptation and medical image segmentation. This combo allows for more precise and progressive recombination of content and style features, addressing limitations in existing one-to-one UDA methods and extending the applicability of domain adaptation techniques in medical imaging.

We summarized our novelty as follows:

* Unsupervised domain adaptation is a mainstream method for addressing domain distribution discrepancies. However, existing UDA methods are mostly limited to one-to-one domain adaptation, which is often inefficient and resource-intensive when dealing with multi-sequence medical image domain adaptation tasks. To address this challenge, we propose a novel and efficient one-to-multiple progressive style transfer UDA framework. It uses a single generator to simultaneously translate one source domain to multiple specified target domains. Compared to one-to-one methods, our approach not only achieves optimal performance but also significantly reduces model parameters and floating-point computation, highlighting its efficiency and feasibility in practical clinical applications.

* We developed a multi-level style dictionary to explicitly store the style information of each target domain at different stages, which alleviates the burden on a single generator in multi-target domain adaptation tasks and achieves effective decoupling of content and style.

* We employed multiple cascaded style fusion modules that progressively utilize point-wise instance normalization to inject local styles into content features. This fine-grained style transfer further reduces domain discrepancies and enhances cross-modal alignment and structural consistency.

Additionally, we have completed the theoretical proof and experimental validation of PIN, bidirectional cross-modal experiments for abdominal multi-organ segmentation, generalization experiments for more target domains, and all supplementary ablation experiments as requested by the reviewers.  

We addressed each comment and question in detail below, and we kindly request the Reviewers **yJwR** and **tgyp** to reconsider our work in light of these aspects and support our efforts. If there are any further questions or concerns, we would be happy to discuss them with you.

We strongly believe that PSTUDA can be a useful addition to the NeurIPS community and above innovative contributions elevate the value and significance of our research in the realm of multi-target medical image domain adaptation and provide useful references and insights for future research.

Thank you very much!



Best regards,

Authors.

Meta-review: The paper receives positive reviewing comments from three reviewers. Two reviewers rate it as 'borderline accept' and a third reviewer rates it as 'strong accept' after rebuttal and discussions. 

The rating by Reviewer tgyp is shown as 'Borderline reject', but in the discussion box, s/he mentioned explicitly that "I suggest changing the score to 5: Borderline Accept." 

Therefore, I recommend 'Accept'.
Decision: Accept (poster)