# Exploring learning transferability in deep segmentation of colorectal cancer liver metastases

Marwan Abbas, Bogdan Badic, Gustavo Andrade-Miranda, Vincent Bourbonne, Vincent Jaouen, Dimitris Visvikis, Pierre-Henri Conze

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## To cite this version:

Marwan Abbas, Bogdan Badic, Gustavo Andrade-Miranda, Vincent Bourbonne, Vincent Jaouen, et al.. Exploring learning transferability in deep segmentation of colorectal cancer liver metastases. Computers in Biology and Medicine, 2025, 198 (Part A), pp.111076. 10.1016/j.compbiomed.2025.111076. hal-05247924

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https://hal.science/hal-05247924v1

Submitted on 7 Oct 2025

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# Exploring learning transferability in deep segmentation of colorectal cancer liver metastases

Marwan Abbas<sup>a,b</sup>, Bogdan Badic<sup>a,c D</sup>, Gustavo Andrade-Miranda<sup>a,b,d D</sup>, Vincent Bourbonne<sup>a,c D</sup>, Vincent Jaouen<sup>a,e D</sup>, Dimitris Visvikis<sup>a D,*</sup>, Pierre-Henri Conze<sup>a,e D</sup>

## ARTICLE INFO

Keywords:

Lesion segmentation

Deep learning

Computed tomography

Semantic segmentation

Learning transferability

Liver metastases

Lesion size

## ABSTRACT

Ensuring the seamless transfer of knowledge and models across various datasets and clinical contexts is of paramount importance in medical image segmentation. This is especially true for liver lesion segmentation which plays a key role in pre-operative planning and treatment follow-up. Despite the progress of deep learning algorithms using Transformers, automatically segmenting small hepatic metastases remains a persistent challenge. This can be attributed to the degradation of small structures due to the intrinsic process of feature down-sampling inherent to many deep architectures, coupled with the imbalance between foreground metastases voxels and background. While similar challenges have been observed for liver tumors originated from hepatocellular carcinoma, their manifestation in the context of liver metastasis delineation remains underexplored and require well-defined guidelines. Through comprehensive experiments, this paper aims to bridge this gap and to demonstrate the impact of various transfer learning schemes from off-the-shelf datasets to a dataset containing liver metastases only. Our scale-specific evaluation reveals that models trained from scratch or with domain-specific pre-training demonstrate greater proficiency.

## 1. Introduction

Accurately detecting and segmenting liver lesions from medical images represents a key challenge in oncology, with significant implications for patient care, surgical planning, treatment planning and follow-up. Recent epidemiological studies indicate a rising incidence of liver metastases, particularly in patients with colorectal cancer [1], underscoring the need for reliable automatic segmentation methodologies. Colorectal cancer ranks as third most commonly diagnosed cancer globally, with over half of affected patients developing liver metastases at diagnosis or during the course of the disease [2]. While advances in imaging technologies have enhanced the ability to visualize hepatic structures, accurately delineating liver metastases, especially small lesions, remains a significant challenge [3].

The emergence of deep learning architectures has transformed the landscape of medical image segmentation [4], introducing unprecedented feature extraction and pattern recognition capabilities. Convolutional neural networks (CNNs), exemplified by architectures such

as U-Net [5], have demonstrated remarkable success in capturing hierarchical features from medical imaging data. These networks excel in identifying local patterns and structural characteristics that are essential for lesion segmentation. Colorectal liver metastases (CRLM) have benefited from these advancements through the use of multi-stage hierarchical cascaded U-Net models [6], demonstrating promising early results and showcasing the feasibility of deep learning approaches in this domain.

Going further, recent innovations in neural architecture design have led to the development of hybrid approaches that combine the strengths of traditional CNNs with advanced attention mechanisms [7]. Transformer-based architectures, including TransUNet [8] and SwinUNet [9], have emerged as promising solutions, offering enhanced capability in modeling global contextual relationships within medical images. These models leverage self-attention mechanisms [10] to capture long-range dependencies, potentially addressing the limitations observed in conventional CNN-based approaches when dealing with

<footnote><sup>a</sup> LaTIM UMR 1101, Inserm, Brest, France</footnote>

<footnote><sup>b</sup> University of Western Brittany, Brest, France</footnote>

<footnote><sup>c</sup> University Hospital of Brest, Brest, France</footnote>

<footnote><sup>d</sup> SyCoIA, IMT Mines Ales, Ales, France</footnote>

<footnote><sup>e</sup> IMT Atlantique, Brest, France</footnote>

<footnote>* Corresponding author.
E-mail address: visvikis@univ-brest.fr (D. Visvikis).</footnote>
complex anatomical structures. Although such models can be effec-
tive for segmenting large liver lesions, their performance on small
hepatic metastases is heavily reliant on the availability of large an-
notated datasets, which are often scarce due to the labor-intensive
nature of image annotation [11,12]. While similar challenges have
been observed in liver tumors originating from hepatocellular carci-
noma (HCC) [13], their manifestation in the context of liver metas-
tasis delineation, particularly from colorectal cancer, remains largely
under-explored.

A core challenge in creating reliable segmentation models stems
from the intrinsic variability present in medical imaging data. Different
imaging protocols, scanner specifications, acquisition and reconstruc-
tion techniques, and institutional practices can introduce significant
domain gaps that impact model performance. The medical imaging
community has responded to these challenges through various learning
transferability schemes aimed at bridging domain gaps and general-
izing knowledge acquired from one domain to another (e.g. between
two imaging data centers). These schemes include domain adapta-
tion strategies [14], such as unsupervised and semi-supervised learn-
ing approaches. Adversarial training [15] and feature alignment [16]
have especially emerged as valuable tools for enhancing model gen-
eralizability across different clinical settings. Data augmentation tech-
niques also play a crucial role in addressing dataset variability by
artificially expanding training samples through controlled transfor-
mations, thereby enhancing model robustness against variations in
medical imaging [17]. However, the variability in patient anatomy,
disease presentation, and image quality within a single institution or
across populations can still lead to model degradation when applied to
unseen data.

Meanwhile, the growing availability of standardized datasets, such
as the Liver Tumor Segmentation (LiTS) dataset [3], has provided cru-
cial resources for developing and validating liver tumor segmentation
approaches. While they offer a common benchmark for comparing
methods, such datasets often represent only a relatively narrow subset
of clinical scenarios, limiting the generalizability of models trained on
them. Moreover, the scarcity of high-quality annotations, particularly
for small lesions, remains a significant limitation in advancing the
field of lesion detection. A critical question remains unresolved for
researchers and clinicians: when faced with limited annotated data
for liver metastasis segmentation, which learning strategy best exploits
publicly available datasets? While systematic transfer learning evalua-
tions exist for HCC, colorectal liver metastases present distinct imaging
characteristics, disease progression patterns, and clinical presentation
that warrant dedicated investigation. This research gap is particularly
significant given that CRLM represent the most common form of liver
metastases, yet lack comprehensive systematic evaluation of optimal
training strategies.

In this context, our research addresses the following practical ques-
tions: how can we best leverage multiple, heterogeneous datasets to
improve the delineation of liver metastases, and what is the impact
of such learning transferability on performance depending on lesion
size (i.e. from small to medium and large lesions)? We provide a com-
prehensive systematic evaluation of 14 training strategies specifically
designed for this clinical context, offering evidence-based guidelines for
optimal method selection. We consider a scenario where off-the-shelf
annotated datasets — general liver lesion [3] and/or CRLM-only [18]
datasets — are available. With the target task of segmenting CRLM
from CT scans, our investigations focus on identifying optimal training
strategies that can leverage such data diversity while maintaining high
contouring robustness across different lesion sizes.

Critical aspects need to be considered: the need to balance model
generalization with specificity for a particular lesion type (i.e. CRLM),
the integration of learning transferability techniques to address dataset
heterogeneity, and the optimization of transfer learning approaches to
maximize the use of available training imaging data. By systematically

examining them, we aim to develop guidelines for training robust seg-
mentation models that deliver consistent and reliable automatic liver
metastasis delineations. In this work, we systematically examine dif-
ferent methodological avenues, including domain-specific pre-training,
the integration of extensive datasets, and the comparative evaluation of
training schemes, spanning from models trained from scratch to those
benefiting from transfer learning. Our focus remains on developing
practical solutions that can effectively address the challenges of liver
metastasis segmentation across all lesion sizes, with a particular atten-
tion to the challenging case of small lesions, while maintaining robust
performance across clinical conditions and imaging parameters.

## 2. Materials and methods

### 2.1. *Datasets*

We aimed to evaluate various learning transferability schemes using three datasets: LiTS, MetaRec and MetaBrest.

*   **LiTS:** The Liver Tumor Segmentation (LiTS) dataset<fnref n="1" /> [3] is a publicly-available imaging data collection designed to promote the advancement of liver lesion analysis from CT scans. Serving as a cornerstone in our investigations of domain adaptation, LiTS comprises 131 scans that capture a broad spectrum of tumor types, with HCC and liver metastases being predominant. Each scan is provided with a manually performed segmentation map delineating liver lesion areas. In our effort to combine data sources for improved segmentation, the LiTS dataset played a crucial role since it provides a multi-faceted landscape of liver lesions, serving as a solid foundation to refine and benchmark different training strategies.

*   **MetaRec:** Pre-operative public CT data for patients undergoing resection of CRLM enriched our research with 197 scans. This public dataset<fnref n="2" /> [18] comprises pre-operative CT scans from patients who underwent surgical resection of CRLM with corresponding liver metastasis segmentation masks.

*   **MetaBrest:** This private in-house dataset, collected at the University Hospital of Brest, France, provides a rich collection of 260 CT scans, with a keen emphasis on CRLM. Each scan was annotated by a clinical expert with more than 3 years of experience in oncologic imaging, ensuring precision in lesion delineation. The delineations were verified afterward by an external reader. The dataset was manually annotated slice-wise using the 3D Slicer software [19].

In our approach, LiTS and MetaRec datasets are denoted as $D_1$ and $D_2$, respectively. In addition, we divided MetaBrest into two subsets: $D_3^{train}$ which comprises 148 scans allocated for training, and $D_3^{test}$ encompassing 112 scans designated as test set. The split was conducted to maintain an even distribution of lesion types (Fig. 1). Indeed, to thoroughly evaluate the segmentation performance, we categorized lesions by volume. Specifically, lesions were classified into three categories: small lesions with a volume smaller than $1000 \text{ mm}^3$ – aligned with the 10 mm diameter threshold defined by the RECIST criteria [20] –, medium lesions with volumes ranging from $1000$ to $25000 \text{ mm}^3$, and large lesions exceeding $25000 \text{ mm}^3$. This latter threshold closely corresponds to those considered as untreatable in radio-frequency treatments (i.e. diameter greater than 3 cm). Furthermore, careful consideration was taken to ensure that all examinations from the same patient were allocated to the same set, thereby maintaining the integrity of patient-specific analyses and alleviating potential biases in the evaluation of our models.

<footnote n="1">¹ https://competitions.codalab.org/competitions/17094</footnote>

<footnote n="2">² Colorectal Liver Metastases dataset</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. Distribution of lesions by size across each dataset: LiTS [3], MetaRec [18] and MetaBrest. Lesion categories (small, medium, large) were defined using 1000 and 25000 mm³ thresholds.</figcaption></figure>

To gain a comprehensive understanding of the distribution for each dataset, Fig. 2 offers a visual comparison. Each dataset exhibits a uniform distribution in terms of volume, with LiTS capturing a wider range of intensities, mainly attributed to the presence of HCC tumors.

## 2.2. Data diversity

Data diversity is a critical component to develop robust and reliable deep learning models, particularly in the medical imaging field. Table 1 reflects a comparison across the three datasets: LiTS ($D_1$), metaRec ($D_2$), and metaBrest ($D_3$), each varying in the number of scans, patient cases, and source attributes. Dataset $D_1$ originates from a public, multi-institution, and multi-device collection featuring a range of primary (e.g. HCC) and secondary (e.g. CRLM) liver pathologies. Scan were acquired from diverse institutions, using various imaging methods and equipments under standard and typical clinical settings, leading to an highly heterogeneous imaging set. In contrast, $D_2$ stems from a mono-site, mono-device setting and focuses exclusively on colorectal cancer with liver metastases, suggesting a more specialized yet less varied composition. The third dataset, $D_3$, encompasses data from a single institution as well as multiple sites and devices, mostly containing inoperable metastases of colorectal cancer treated with chemotherapy. This mix of sources brings a range of variability, allowing for detailed experiments on which data types and combinations, along with which methods (e.g. training models from scratch, using pre-trained weights) offer the best approaches for delineating liver metastases, particularly the smallest ones.

A closer analysis of the main dataset characteristics (Table 7) reveals comparable distributions across the training and test groups in key parameters such as in-plane resolution, slice thickness, volume size, number of tumors, tumor volume, and liver volume. This consistency indicates that the splitting process preserved the inherent diversity of the dataset. A multi-faceted dataset enhances the training process by providing a wide array of scenarios, challenging the models to adapt to different data distributions, ultimately leading to more accurate and universally applicable outcomes in clinical settings. Table 8 provides a comprehensive understanding of the institutional diversity and technical specifications from which the datasets were derived. It details the number of contributing sites and the variety of acquisition machines across datasets, showing further insight into the heterogeneity of the data and the potential biases associated with specific imaging technologies and protocols.

## 2.3. Network architecture

To comprehensively evaluate the impact of various learning schemes, we employed three distinct representative neural network architectures for liver lesion segmentation: U-Net, UNETR, and Swin-UNETR. This architectural comparison allows to assess whether learning findings are architecture-specific or generalizable across different network designs.

* **U-Net:** The U-Net architecture [5] serves as baseline convolutional neural network. This encoder-decoder architecture with skip connections has demonstrated remarkable success in medical image segmentation tasks. The network consists of a contracting path that captures context through pooling operations and an expansive path that enables precise localization through upsampling. While U-Net excels at capturing local spatial patterns and structural characteristics essential for lesion segmentation, its reliance on convolutional operations limits its ability to model long-range dependencies that may be crucial for distinguishing small lesions from surrounding healthy tissues.

* **UNETR:** The UNETR architecture [21] represents a hybrid approach that combines a Vision Transformer (ViT) [22] encoder with a CNN-based decoder. This design leverages self-attention mechanisms to capture global contextual relationships while maintaining the proven effectiveness of convolutional decoders for dense prediction tasks. UNETR processes medical images as sequences of patches, enabling the modeling of long-range spatial dependencies that conventional CNNs may miss. However, the standard ViT encoder treats all spatial locations equally, which may not be optimal for medical images where hierarchical feature representations are beneficial.

* **SwinUNETR:** In this work, we employed the SwinUNETR architecture [9] as our primary architecture for liver lesion segmentation purposes due to its state-of-the-art performance for organ CT segmentation. The SwinUNETR network is a modification of the classical U-Net architecture [5], integrating the Transformer-based mechanisms from the Swin Transformer [23]. This architecture combines the inductive bias derived from CNN-like features with local attention using shifted windows. It starts with small-sized patches and gradually merges neighboring patches in subsequent layers, enabling precise contouring for complex delineation tasks [9]. Moreover, SwinUNETR was selected for its proficiency in conducting robust transfer learning experiments, which provides a flexible framework for adapting pre-trained models to new tasks. On one side, this approach facilitates the exploration of various transfer learning strategies. On the other side, the compatibility of SwinUNETR with self-supervised pre-trained weights from extensive experiments [24] presents an opportunity to harness the potential of mega datasets. This dual capability underscores the architecture strength in adapting to large-scale and diverse medical imaging challenges.

## 2.4. Loss function

To train the models, we leveraged two distinct loss functions: the soft Dice loss and the cross-entropy loss. Let $I$ represents the total number of voxels, and $J$ is the number of classes. $G_{i,j}$ denotes the ground truth, and $Y_{i,j}$ is the predicted probability for class $j$ at voxel $i$. The soft Dice loss is defined as:

$$ \mathcal{L}_{\text{Dice}}(G, Y) = 1 - \frac{2}{J} \sum_{j=1}^{J} \frac{\sum_{i=1}^{I} G_{i,j} Y_{i,j}}{\sum_{i=1}^{I} G_{i,j}^{2} + \sum_{i=1}^{J} Y_{i,j}^{2}}, \quad (1) $$

and measures the overlap between predicted and ground truth delinations. On its turn, the cross-entropy loss is given by:

$$ \mathcal{L}_{\text{CE}}(G, Y) = -\frac{1}{I} \sum_{i=1}^{I} \sum_{j=1}^{J} G_{i,j} \log(Y_{i,j}), \quad (2) $$
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Scatter plot of tumor volume (in mm³) versus mean intensity (HU) for LiTS [3], MetaRec [18] and MetaBrest datasets. Each dot corresponds to a single liver lesion, color-coded according to its source dataset (blue for LiTS, green for MetaBrest, orange for MetaRec). The vertical green dotted lines at 1000 mm³ and 25000 mm³ indicate volume thresholds used for lesion size categorization. The colored areas represent kernel density estimations (KDE) with 95% probability density regions, with LiTS encompassing a wider range of lesion intensity values compared to the other datasets used for training, notably for small lesions (< 1000 mm³).</figcaption></figure>

<table><caption>**Table 1**
Comparison between LiTS [3], MetaRec [18] and MetaBrest datasets.</caption><thead><tr><th>Attribute</th><th>LiTS (D<sub>1</sub>)</th><th>metaRec (D<sub>2</sub>)</th><th>metaBrest (D<sub>3</sub>)</th></tr></thead><tbody><tr><td>Nb. of scans</td><td>131</td><td>197</td><td>260</td></tr><tr><td>Nb. of patients</td><td>/</td><td>197</td><td>171</td></tr><tr><td>Availability</td><td>Public</td><td>Public</td><td>Private</td></tr><tr><td>Acquisition</td><td>Multi-institution, Multi-site, Multi-device</td><td>Mono-site, Mono-device</td><td>Single-institution, Multi-site, Multi-device</td></tr><tr><td>Lesions types</td><td>Hepatic tumors, metastases</td><td>Operable metastases</td><td>Inoperable metastases (treated with chemo)</td></tr><tr><td>Etiology</td><td>Multiple pathologies</td><td>Colorectal cancer</td><td>Colorectal cancer</td></tr><tr><td>Source</td><td>http://medicaldecathlon.com/</td><td>cancerimagingarchive.net/collection/colorectal-liver-metastases/</td><td>/</td></tr></tbody></table>

and assesses the pixel-wise discrepancy between the predicted probability and the actual label. Combining these, we formulated the global loss function following:

$$ \mathcal{L}(G, Y) = \mathcal{L}_{\text{Dice}}(G, Y) + \mathcal{L}_{\text{CE}}(G, Y). \quad (3) $$

## 2.5. Training strategies

To assess the performance and adaptability of the models for segmenting liver metastases from CT scans, we designed 14 training scenarios divided into three main families: from scratch (Section 2.5.1), domain-specific (Section 2.5.2) and off-the-shelf pre-training (Section 2.5.3) strategies. They offer insights into the influence of data utilization, weight initialization, and how the integration of different datasets influence performance.

### 2.5.1. From scratch strategies

From scratch learning strategies (Fig. 3) were built as follows:

* **Strategies 1 and 2.** We followed a straightforward training strategy where each network was exclusively trained on external public datasets. Specifically, the network was trained on the $D_1$ dataset for Strategy 1 and on the $D_2$ dataset for Strategy 2, both strategies being subsequently tested on $D_3^{\text{test}}$.

* **Strategy 3.** We employed a direct training approach using only $D_3^{\text{train}}$ and subsequently tested it on $D_3^{\text{test}}$. The idea was to investigate the direct knowledge transfer between data arising from MetaBrest only, i.e. when the target dataset is the same than the one used in training.

* **Strategies 4 and 5.** In those approaches, the networks were trained using a combination of datasets. Specifically, for Strategy 4, the network was trained on the concatenated set $\{D_2, D_3^{\text{train}}\}$, whereas Strategy 5 was trained on the concatenated set $\{D_1, D_2, D_3^{\text{train}}\}$. Both strategies were then validated on $D_3^{\text{test}}$ to explore the benefits of joint training and its impact on generalization to a known dataset.

### 2.5.2. Domain-specific pre-training strategies

We took a step further by assessing the influence of pre-trained weights in a fine-tuning fashion (Fig. 4). To achieve this, we used the pre-trained weights acquired from domain-specific datasets, i.e. sets containing only CT scans with liver tumors.

* **Strategy 6.** Pre-trained weights from the models, initially trained on the $D_1$ dataset, were fine-tuned on the $D_2$ dataset. The resulting model was then tested on $D_3^{\text{test}}$ to evaluate the performance after fine-tuning on different external datasets.

* **Strategy 7 and 8.** Pre-trained weights from the models were initially trained on different external datasets: $D_1$ for Strategy 7 and $D_2$ for Strategy 8. In both strategies, the pre-trained weights were directly fine-tuned on the $D_3^{\text{train}}$ dataset. The models were subsequently tested on the $D_3^{\text{test}}$ dataset to assess the impact of direct fine-tuning on the target dataset.

* **Strategy 9.** Pre-trained weights from the models, obtained after double fine-tuning (initially on the $D_1$ dataset and then on the $D_2$ dataset, as in Strategy 6), were further fine-tuned on the $D_3^{\text{train}}$ dataset. The final model was tested on the $D_3^{\text{test}}$ dataset to evaluate the cumulative effects of multi-stage fine-tuning.
<figure><img src="image_6.png" /><figcaption>Fig. 3. Overview of various from scratch training strategies employed for liver metastasis segmentation purposes.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 4. Overview of various fine-tuning training strategies employed for liver metastasis segmentation purposes. Blue arrows represent the transfer of domain-specific pre-trained weights during the fine-tuning process.</figcaption></figure>

### 2.5.3. Off-the-shelf pre-training strategies

For the last five training scenarios, we aimed at assessing the impact of weight initialization through self-supervised learning. To achieve this, we employed pre-trained weights from models which had been trained on a 3D mega-dataset of roughly 50,000 images (Table 9) using self-supervised learning [24]. The main objective was to investigate the advantages of weight initialization in an ImageNet-like manner to tackle the task of metastasis segmentation.

* **Strategy 1_bis** to **5_bis**. We initialized the networks with the pre-trained weights obtained from self-supervised learning. Subsequently, the models were fine-tuned using the same sets as in Section 2.5.1 and tested on $D_3^{test}$.

These diverse strategies tend to enable a nuanced understanding of data interplay, the benefits of pre-training, and the network's robustness across different scenarios.

## 3. Experiments

### 3.1. Pre-processing

Prior to training segmentation models, CT scans underwent several pre-processing steps. Volumes were interpolated to a voxel spacing of 1.5×1.5×2.0 mm<fnref n="3" />. Abdominal windowing followed by image normalization between 0 and 1 were applied for enhanced lesion visibility and contrast within hepatic tissue. The region of interest (i.e. the liver area) was first extracted for each scan and then, random sampling of sub-volumes of size 96 × 96 × 96 voxels was performed with positive and negative ratio of 1:1. Data augmentation techniques were used including random flip, rotation and intensity shifting with probabilities of 0.1, 0.1, and 0.5 respectively.

### 3.2. Implementation details

We used PyTorch and MONAI<fnref n="3" /> for model implementation. All three architectures (U-Net, UNETR, SwinUNETR) were implemented using MONAI's standardized implementations to ensure fair comparisons. The networks were trained for 500 epochs using the AdamW optimizer with a batch size of 4, and an initial learning rate of $4 \times 10^{-4}$ for strategies without weight initialization. Conversely, strategies with weight initialization employed a reduced learning rate of $4 \times 10^{-5}$. A decay rate of 0.02 was applied when validation loss plateaued for 10 epochs, with early stopping triggered if no decrease occurred over 30 epochs. The segmentation loss comprised both Dice and cross-entropy losses with equal weight (Eq. (3)). Training was conducted on 4 Nvidia V100 GPUs with 32Gb of RAM for each. Architectural-specific hyperparameters were optimized individually for each model. The detailed training hyper-parameters can be found in Table 2.

Inference was performed using a sliding window technique with 80% overlap, framed as a 3-class segmentation task identifying background, liver, and tumors. By incorporating the liver as a separate class, the model not only learns to distinguish tumors from the surrounding liver tissues more effectively but also allows for a crucial post-processing step: discarding tumor delineations outside the predicted liver region, thus reducing the generation of false positives.

### 3.3. Segmentation assessment

To assess the accuracy of delineation results, we employed two distinct evaluation metrics. The first metric, Avg Global Dice, computes the Dice score for each individual examination and then derives an overall assessment by averaging these scores across the dataset. The Dice similarity coefficient (Dice) is defined for each individual examination as:

$$ \text{Dice}(E_n) = \frac{2 \sum_{i=1}^{I} Y_{i,n} \hat{Y}_{i,n}}{\sum_{i=1}^{I} Y_{i,n} + \sum_{i=1}^{I} \hat{Y}_{i,n}}, \quad (4) $$

where $Y_{i,n}$ represents the ground truth, and $\hat{Y}_{i,n}$ is the predicted probability for voxel $i$ in examination $E_n$.

The average global Dice across all examinations in $D_3^{test}$ is then calculated as:

$$ \text{Avg Global Dice} = \frac{1}{N} \sum_{n=1}^{N} \text{Dice}(E_n), \quad (5) $$

where $N$ is the number of examinations. This metric gives an overall assessment by averaging the Dice scores from each examination, but

<footnote n="3">³ https://monai.io</footnote>
<table><caption>**Table 2**
Comprehensive architectural and training specifications.</caption><thead><tr><th>Category</th><th>Parameter</th><th>U-Net</th><th>UNETR</th><th>SwinUNETR</th></tr></thead><tbody><tr><td rowspan="7">Training configuration</td><td>Sample dimensions</td><td>(96, 96, 96)</td><td>(96, 96, 96)</td><td>(96, 96, 96)</td></tr><tr><td>Target spacing (mm³)</td><td>1.5 × 1.5 × 2</td><td>1.5 × 1.5 × 2</td><td>1.5 × 1.5 × 2</td></tr><tr><td>Training epochs</td><td>500</td><td>500</td><td>500</td></tr><tr><td>Batch size</td><td>4</td><td>4</td><td>4</td></tr><tr><td>Optimizer</td><td>AdamW</td><td>AdamW</td><td>AdamW</td></tr><tr><td>Learning rate</td><td>4 × 10⁻⁴</td><td>4 × 10⁻⁴</td><td>4 × 10⁻⁴ – 4 × 10⁻⁵</td></tr><tr><td>LR scheduling</td><td>Cosine annealing</td><td>Cosine annealing</td><td>Cosine annealing</td></tr><tr><td rowspan="7">Network architecture</td><td>Loss function</td><td>Dice + Cross-entropy</td><td>Dice + Cross-entropy</td><td>Dice + Cross-entropy</td></tr><tr><td>CNN initial features</td><td>32</td><td>–</td><td>–</td></tr><tr><td>Encoder depths</td><td>[1,2,2,4]</td><td>–</td><td>–</td></tr><tr><td>Decoder depths</td><td>[4,2,2,1]</td><td>–</td><td>–</td></tr><tr><td>Patch size</td><td>–</td><td>(16,16,16)</td><td>(4,4,4)</td></tr><tr><td>Hidden dimension</td><td>–</td><td>768</td><td>768</td></tr><tr><td>Embedding dimension</td><td>–</td><td>768</td><td>768</td></tr><tr><td rowspan="6">Regularization &amp; Optimization</td><td>Feature size</td><td>–</td><td>16</td><td>48</td></tr><tr><td>Transformer blocks</td><td>–</td><td>12</td><td>[2,2,2,2]</td></tr><tr><td>Window size</td><td>–</td><td>–</td><td>[7,7,7]</td></tr><tr><td>Attention heads</td><td>–</td><td>12</td><td>[3,6,12,24]</td></tr><tr><td>MLP expansion</td><td>–</td><td>4.0</td><td>4.0</td></tr><tr><td>Weight decay</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Early stopping</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Dropout layers</td><td>×</td><td>✓</td><td>×</td></tr><tr><td>Stochastic depth</td><td>×</td><td>×</td><td>×</td></tr><tr><td>Mixed precision</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

may produce false positives, such as segmenting liver cysts as metastases. It can also be influenced by the presence of larger lesions, which may lead to skewed results.

The second metric, Avg Dice/lesion, adopts a more granular approach by treating each lesion as a separate entity delineated by bounding boxes and then computing the average Dice score across all individual lesions (Fig. 5).

This metric is defined as :

$$ \text{Avg Dice/lesion} = \frac{1}{N_{\text{total}}} \sum_{k=1}^{N_{\text{total}}} \text{Dice}(L_k), \qquad (6) $$

where $N_{total}$ is the total number of lesions across all sizes, and $L_k$ is the kth lesion regardless of size.

To further refine our evaluation, we have derived the Avg Dice/lesion metric into supplementary metrics based on lesion size: small, medium, and large. This sub-division enables a thorough assessment of the performance across varying lesion sizes. In particular, it ensures a comprehensive assessment of the capability to precisely segment smaller lesions, which are typically more challenging to delineate. For a compact representation of the average Dice scores for different lesion sizes, we define:

$$ \text{Avg Dice}_{\text{size}} = \frac{1}{N_{\text{size}}} \sum_{k=1}^{N_{\text{size}}} \text{Dice}(L_{k,\text{size}}), \qquad (7) $$

where 'size' is substituted with small, medium, or large, $L_{k,\text{size}}$ denotes the kth lesion of the given size, and $N_{\text{size}}$ is the total number of lesions for that size.

### 3.4. Instance assessment

Beyond voxel-level assessment, we evaluate our models at the lesion level, which more closely aligns with clinical practice where radiologists assess individual metastases. We use two complementary approaches to instance-level evaluation.

First, for detection rate analysis purposes, we generate bounding boxes around each ground truth lesion to quantify the proportion of lesions successfully identified by each model. A lesion is considered “detected” when there is a non-zero overlap between the predicted segmentation and the ground truth bounding box (Dice>0). To ensure

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Illustration of Avg Dice/lesion metric evaluation. The green, blue, and orange bounding boxes respectively represent large, medium, and small hepatic metastases.</figcaption></figure>

clinical relevance, we also evaluate using a more stringent threshold (Dice≥0.25) that requires more substantial overlap for a lesion to be considered properly detected. Detection rates are calculated for different lesion size categories, providing insight into model sensitivity across small, medium, and large metastases.

Second, for a more detailed assessment of instance-level performance, we use a 3D connected component analysis to quantify false positives (FP), false negatives (FN), and true positives (TP). We adopt the instance-level definition from [25] where failure modes are defined as follows:

* FP: instance of a given category that is present in the predicted mask but has zero intersection with the same category in the ground truth mask.

* FN: instance of a given category that is present in the ground truth mask but is completely overlooked (i.e. no intersection with the same category in the prediction).

* TP: instance in the ground truth mask that has a non-zero overlap with the predicted mask.

From these instance-level detections, precision, recall, and F1-score metrics can be derived.
<caption>**Table 3**</caption>

Architectural comparison results for Strategies 1–5 evaluated on the test set ($D_3^{test}$) of MetaBrest dataset. Results show Dice coefficient with standard deviations. Best results for each architecture are shown in bold.

<table><thead><tr><th>Architecture</th><th>Strategy</th><th>Avg Global Dice</th><th>Avg Dice/lesion</th><th>Dice (small)</th><th>Dice (medium)</th><th>Dice (large)</th></tr></thead><tbody><tr><td rowspan="5">U-Net</td><td>1</td><td>0.644 ± 0.246</td><td>0.623 ± 0.252</td><td>0.482 ± 0.296</td><td>0.720 ± 0.194</td><td>0.792 ± 0.165</td></tr><tr><td>2</td><td>0.685 ± 0.234</td><td>0.561 ± 0.310</td><td>0.372 ± 0.359</td><td>0.724 ± 0.218</td><td>0.823 ± 0.097</td></tr><tr><td>3</td><td>0.677 ± 0.248</td><td>0.615 ± 0.275</td><td>0.455 ± 0.325</td><td>0.754 ± 0.170</td><td>0.837 ± 0.135</td></tr><tr><td>4</td><td>0.683 ± 0.240</td><td>0.634 ± 0.270</td><td>0.486 ± 0.324</td><td>0.751 ± 0.175</td><td>0.845 ± 0.115</td></tr><tr><td>5</td><td><strong>0.698 ± 0.238</strong></td><td><strong>0.646 ± 0.258</strong></td><td><strong>0.495 ± 0.315</strong></td><td><strong>0.765 ± 0.148</strong></td><td><strong>0.846 ± 0.123</strong></td></tr><tr><td rowspan="5">UNETR</td><td>1</td><td>0.678 ± 0.241</td><td>0.666 ± 0.248</td><td><strong>0.544 ± 0.282</strong></td><td>0.745 ± 0.188</td><td>0.814 ± 0.157</td></tr><tr><td>2</td><td>0.713 ± 0.226</td><td>0.604 ± 0.308</td><td>0.427 ± 0.349</td><td>0.740 ± 0.203</td><td>0.841 ± 0.092</td></tr><tr><td>3</td><td>0.706 ± 0.242</td><td>0.652 ± 0.268</td><td>0.496 ± 0.311</td><td>0.771 ± 0.162</td><td>0.842 ± 0.130</td></tr><tr><td>4</td><td>0.715 ± 0.235</td><td>0.678 ± 0.262</td><td>0.525 ± 0.315</td><td>0.770 ± 0.168</td><td>0.853 ± 0.112</td></tr><tr><td>5</td><td><strong>0.720 ± 0.232</strong></td><td><strong>0.688 ± 0.253</strong></td><td>0.540 ± 0.305</td><td><strong>0.785 ± 0.142</strong></td><td><strong>0.854 ± 0.118</strong></td></tr><tr><td rowspan="5">SwinUNETR</td><td>1</td><td>0.703 ± 0.238</td><td>0.698 ± 0.243</td><td><strong>0.591 ± 0.277</strong></td><td>0.765 ± 0.183</td><td>0.824 ± 0.154</td></tr><tr><td>2</td><td>0.735 ± 0.221</td><td>0.641 ± 0.302</td><td>0.463 ± 0.336</td><td>0.755 ± 0.200</td><td>0.850 ± 0.088</td></tr><tr><td>3</td><td>0.725 ± 0.236</td><td>0.691 ± 0.262</td><td>0.543 ± 0.304</td><td>0.787 ± 0.157</td><td>0.856 ± 0.126</td></tr><tr><td>4</td><td>0.738 ± 0.229</td><td>0.709 ± 0.256</td><td>0.578 ± 0.304</td><td>0.792 ± 0.160</td><td><strong>0.864 ± 0.107</strong></td></tr><tr><td>5</td><td><strong>0.743 ± 0.226</strong></td><td><strong>0.718 ± 0.244</strong></td><td>0.588 ± 0.295</td><td><strong>0.802 ± 0.138</strong></td><td>0.863 ± 0.113</td></tr></tbody></table>

# 4. Results

## 4.1. Architecture comparison

Our comparative evaluation across three distinct architectures (U-Net, UNETR, SwinUNETR) on the baseline strategies (1–5) reveals critical insights into both architectural capabilities and learning robustness. Table 3 presents comprehensive results demonstrating that learning principles remain consistent across different network designs while highlighting significant architectural performance differences. Indeed, the most significant finding is the preservation of strategy performance hierarchy across all architectures. Strategy 5 consistently achieves the highest performance regardless of architecture (0.698±0.238 for U-Net, 0.720±0.232 for UNETR, 0.743±0.226 for SwinUNETR), followed by strategies incorporating the diverse LiTS dataset. Strategy 2 (MetaRec only) demonstrates relatively poor performance across all architectures, particularly for small lesions where U-Net achieves only 0.372±0.359, confirming that dataset diversity and heterogeneity are fundamental requirements independent of network design. This consistency validates those learning insights represent universal principles rather than architecture-specific phenomena.

However, substantial absolute performance differences emerge between architectures. SwinUNETR demonstrates superior performance across all evaluation metrics, with the most pronounced advantages in small lesion segmentation. For small lesions (< 1000 mm³), SwinUNETR outperforms U-Net by 8.9–12.6% and UNETR by 4.4–4.7% across all strategies. Architectural differences are less pronounced for large lesions, where all networks achieve relatively high performance (0.792–0.863 in Dice range), but become increasingly significant as lesion size decreases. For medium lesions, the performance gaps narrow but remain consistent, with SwinUNETR achieving 0.765–0.802 across strategies compared to 0.720–0.765 for U-Net. Those patterns suggest that the hierarchical feature learning and shifted window attention mechanisms of SwinUNETR are particularly advantageous for detecting subtle lesion boundaries and distinguishing small metastases from surrounding liver tissue. The consistent architectural ranking (SwinUNETR > UNETR > U-Net) across all five baseline strategies confirms that transformer-based approaches with hierarchical design offer measurable advantages for liver metastasis segmentation, justifying focused analysis on SwinUNETR for advanced transfer learning strategies in the following ablation studies.

Based on this preliminary architectural comparison results, SwinUNETR demonstrated superior performance across all evaluation metrics, particularly for small lesion segmentation. Therefore, detailed transfer learning analysis (Strategies 6–14) was conducted exclusively with SwinUNETR to maximize the impact of our comprehensive evaluation.

## 4.2. Segmentation performance

With architectural superiority established, we conducted comprehensive transfer learning analysis using SwinUNETR across all 14 training strategies to provide more insights into optimal transfer learning approaches for liver metastasis segmentation. As detailed in Table 4, several key performance patterns emerge across the evaluated strategies.

First, when examining the performance by lesion size, we observe a consistent trend where segmentation accuracy decreases as lesion size diminishes. For large lesions (> 25000 mm³), all strategies achieve relatively high Dice scores ranging from 0.793 to 0.872. For medium lesions (1000–25000 mm³), performance moderately decreases across all strategies. The most significant performance drop occurs with small lesions, where even the best-performing Strategy 7 only reaches a Dice score of 0.602. Notably, the performance gap between the best and worst strategies is much more pronounced for small lesions (Dice score difference of 0.139) than for large lesions (0.079), highlighting small lesion segmentation as the primary differentiating factor among evaluated approaches.

When examining strategy families in detail, we observe that from scratch approaches utilizing either multiple datasets (Strategies 4 and 5) or a single heterogeneous dataset (Strategy 1) demonstrate consistently strong performance across the small-scale metric. In contrast, self-supervised strategies do not appear to bring added value compared to from scratch schemes. They either maintain comparable performance to their non-initialized counterparts or exhibit slight performance degradation across some metrics, especially for large and medium lesions. Even the domain-specific pre-training strategies that perform well (such as Strategy 7 and 9) derive their effectiveness from exposure to relevant domain-specific data (LiTS) rather than from generic initialization patterns.

In summary, our quantitative evaluation across 14 training strategies reveals that data diversity and domain specificity are more critical to CRLM segmentation performance than generic pre-training schemes. The highest performing approaches consistently incorporate the heterogeneous LiTS dataset, either through direct training or domain-specific pre-training. The performance gap between strategies is most significant for small lesions, making them the focus for further methodological assessment. These findings suggest that future development should prioritize exposure to diverse lesion characteristics rather than generic self-supervised initialization. In addition, while direct comparison with previous work is challenging due to dataset variations and lesion types, our results demonstrate improvements compared to existing works dedicated to small lesion segmentation [6,13], which remains a key challenge in CRLM analysis.
<caption>**Table 4**</caption>

Quantitative results for each training strategy obtained on the test set ($D_3^{test}$) of MetaBrest dataset using SwinUNETR. The presented measure is the Dice coefficient along with associated standard deviations. The best and second-best results are shown in bold and underlined, respectively.

<table><thead><tr><th>Strategy</th><th>Avg Global Dice</th><th>Avg Dice/lesion</th><th>Dice (small)</th><th>Dice (medium)</th><th>Dice (large)</th></tr></thead><tbody><tr><td>1</td><td>0.703 ± 0.238</td><td>0.698 ± 0.243</td><td>0.591 ± 0.277</td><td>0.765 ± 0.183</td><td>0.824 ± 0.154</td></tr><tr><td>2</td><td>0.735 ± 0.221</td><td>0.641 ± 0.302</td><td>0.463 ± 0.336</td><td>0.755 ± 0.200</td><td>0.850 ± 0.088</td></tr><tr><td>3</td><td>0.725 ± 0.236</td><td>0.691 ± 0.262</td><td>0.543 ± 0.304</td><td>0.787 ± 0.157</td><td>0.856 ± 0.126</td></tr><tr><td>4</td><td>0.738 ± 0.229</td><td>0.709 ± 0.256</td><td>0.578 ± 0.304</td><td>0.792 ± 0.160</td><td>0.864 ± 0.107</td></tr><tr><td>5</td><td>0.743 ± 0.226</td><td><u>0.718 ± 0.244</u></td><td>0.588 ± 0.295</td><td><u>0.802 ± 0.138</u></td><td>0.863 ± 0.1132</td></tr><tr><td>6</td><td>0.733 ± 0.228</td><td>0.683 ± 0.272</td><td>0.533 ± 0.318</td><td>0.777 ± 0.170</td><td>0.858 ± 0.075</td></tr><tr><td>7</td><td>0.732 ± 0.228</td><td><b>0.718 ± 0.240</b></td><td><b>0.602 ± 0.287</b></td><td>0.791 ± 0.153</td><td>0.852 ± 0.131</td></tr><tr><td>8</td><td><b>0.756 ± 0.206</b></td><td>0.704 ± 0.252</td><td>0.569 ± 0.298</td><td>0.790 ± 0.156</td><td><u>0.866 ± 0.088</u></td></tr><tr><td>9</td><td><u>0.756 ± 0.210</u></td><td>0.716 ± 0.249</td><td>0.586 ± 0.300</td><td><u>0.798 ± 0.147</u></td><td><b>0.872 ± 0.087</b></td></tr><tr><td>1_bis</td><td>0.655 ± 0.253</td><td>0.705 ± 0.246</td><td>0.593 ± 0.294</td><td>0.779 ± 0.165</td><td>0.819 ± 0.142</td></tr><tr><td>2_bis</td><td>0.721 ± 0.218</td><td>0.640 ± 0.294</td><td>0.477 ± 0.329</td><td>0.745 ± 0.199</td><td>0.822 ± 0.150</td></tr><tr><td>3_bis</td><td>0.664 ± 0.254</td><td>0.630 ± 0.288</td><td>0.486 ± 0.326</td><td>0.723 ± 0.203</td><td>0.793 ± 0.180</td></tr><tr><td>4_bis</td><td>0.701 ± 0.227</td><td>0.665 ± 0.280</td><td>0.509 ± 0.319</td><td>0.771 ± 0.178</td><td>0.816 ± 0.151</td></tr><tr><td>5_bis</td><td>0.696 ± 0.248</td><td>0.715 ± 0.241</td><td><u>0.594 ± 0.289</u></td><td>0.794 ± 0.147</td><td>0.844 ± 0.138</td></tr></tbody></table>

### 4.3. Detection rate performance

While Dice coefficient analysis provides valuable insights into segmentation accuracy at the voxel level, it does not fully capture a model's ability to detect distinct lesion instances, a capability that has significant clinical implications. Therefore, we extend our evaluation to include detection rates, which quantify the proportion of lesions successfully identified by each strategy, providing complementary information on model sensitivity across the different lesion size categories. The detection rates (Table 5) quantify true positive and false negative detections using bounding boxes constructed around each ground truth lesion (as defined in Section 3.3). Our analysis reveals that while performance is comparable across strategies for medium and large lesions, significant differences emerge for small lesions.

For small lesions, Strategy 1 achieved the best detection rate (90.5% for Dice ≥ 0), followed closely by Strategy 7 (89.6%). This suggests that both LiTS pre-training and direct training approaches can effectively capture the features necessary for small lesions detection. When considering a more stringent threshold (Dice ≥ 0.25), both strategies maintain strong performance (84.3% and 83.7% respectively), indicating that performing direct training on diverse external data (LiTS) or pre-training on that same dataset followed by fine-tuning on target data ($D_3^{train}$) can produce precise small lesion detections.

This evaluation block emphasizes that while most strategies perform similarly for larger targets (with detection rates superior to 97% for medium lesions and even reaching 100% for large lesions in many scenarios), accurately detecting and segmenting small CRLM remains challenging. This challenge is particularly critical for early intervention and treatment planing, since newly detected lesions are often small in size. Strategies that integrate data diversity achieve balanced performance across all lesion sizes and reduce false negative rates in small lesion detection.

### 4.4. Statistical analysis

While numeric comparisons of Dice scores provide initial insights into strategy performance, statistical analysis is essential to determine whether the observed differences are significant enough to influence strategy selection in clinical implementation. This analysis is particularly important when evaluating approaches that may be deployed in oncology applications where the reliable delineation of small lesions can impact treatment decisions. To further compare the performance of the top performing strategies, we conducted pairwise comparisons (Fig. 6) focusing on the small-size-scale Dice metric defined in Section 3.3. This metric was chosen because previous Dice-based comparisons showed similar performances across large and medium targets, making small lesions the critical differentiator.

<caption>**Table 5**</caption>

Detection rates of SwinUNETR for different training strategies and lesion sizes using Dice score thresholds >0 and >0.25. SL, ML and LL respectively stand for small, medium, and large hepatic metastases. Best and second-best results are shown in bold and underlined, respectively.

<table><thead><tr><th rowspan="2">Strategy</th><th colspan="2">SL (%)</th><th colspan="2">ML (%)</th><th colspan="2">LL (%)</th><th colspan="2">Avg (%)</th></tr><tr><th>>0</th><th>>0.25</th><th>>0</th><th>>0.25</th><th>>0</th><th>>0.25</th><th>>0</th><th>>0.25</th></tr></thead><tbody><tr><td>1</td><td><b>90.5</b></td><td><b>84.3</b></td><td>98.4</td><td>96.3</td><td><b>100</b></td><td>97.6</td><td><b>95.2</b></td><td><b>91.4</b></td></tr><tr><td>2</td><td>74.3</td><td>66.9</td><td>97.8</td><td>94.7</td><td><b>100</b></td><td><b>100</b></td><td>88.1</td><td>83.5</td></tr><tr><td>3</td><td>86.4</td><td>77.8</td><td><u>98.9</u></td><td><u>97.6</u></td><td><b>100</b></td><td>97.6</td><td>93.6</td><td>89.3</td></tr><tr><td>4</td><td>85.5</td><td>80.5</td><td>98.4</td><td>97.1</td><td><b>100</b></td><td><b>98.8</b></td><td>93.1</td><td>90.3</td></tr><tr><td>5</td><td>86.4</td><td>81.9</td><td><b>99.5</b></td><td><b>98.1</b></td><td><b>100</b></td><td><b>98.8</b></td><td>94.0</td><td><u>91.4</u></td></tr><tr><td>6</td><td>81.9</td><td>75.4</td><td>98.4</td><td>96.6</td><td><b>100</b></td><td><b>100</b></td><td>91.6</td><td>88.0</td></tr><tr><td>7</td><td><u>89.6</u></td><td><u>83.7</u></td><td><u>98.9</u></td><td><u>97.6</u></td><td><b>100</b></td><td>97.6</td><td><u>95.1</u></td><td><b>91.8</b></td></tr><tr><td>8</td><td>86.6</td><td>80.5</td><td><b>98.9</b></td><td>97.3</td><td><b>100</b></td><td><b>100</b></td><td>93.9</td><td>90.5</td></tr><tr><td>9</td><td>87.3</td><td>80.1</td><td>98.7</td><td>97.3</td><td><b>100</b></td><td><b>100</b></td><td>94.0</td><td>90.4</td></tr><tr><td>1_bis</td><td>87.9</td><td>82.5</td><td>98.7</td><td>96.0</td><td><b>100</b></td><td>97.6</td><td>94.2</td><td>90.5</td></tr><tr><td>2_bis</td><td>78.4</td><td>67.4</td><td>97.9</td><td>93.9</td><td><b>100</b></td><td>97.6</td><td>89.9</td><td>83.1</td></tr><tr><td>3_bis</td><td>78.1</td><td>70.1</td><td>97.6</td><td>95.2</td><td><b>100</b></td><td>96.4</td><td>89.6</td><td>84.8</td></tr><tr><td>4_bis</td><td>81.6</td><td>72.5</td><td>97.3</td><td>96.6</td><td><b>100</b></td><td><b>98.8</b></td><td>91.0</td><td>86.6</td></tr><tr><td>5_bis</td><td>87.9</td><td>82.8</td><td>98.1</td><td>97.1</td><td><b>100</b></td><td>97.6</td><td>94.0</td><td>91.3</td></tr></tbody></table>

The analysis revealed no statistically significant differences among the top-performing strategies (Strategies 7, 5_bis, 1_bis, 1, 5, and 9), despite small numerical gaps in their Dice scores. This suggests that these approaches, while employing different training paradigms, achieve statistically comparable performance on the challenging task of small lesion segmentation.

The critical insight from this statistical analysis is that all top-performing strategies share a common element: they all utilize the LiTS dataset ($D_1$), which is the most heterogeneous dataset in our study. This finding reinforces the idea that exposure to diverse lesion presentations, imaging characteristics, and technical parameters is key for successfully delineating small lesions, regardless of whether this exposure comes through direct training, pre-training, or as part of a combined dataset approach.

Fig. 7 shows qualitative segmentation results for Strategies 1, 2, 7, and 9, allowing visual comparison of their performance on representative cases. Strategy 5 was omitted from the visual comparison to maintain clarity while still representing both top-performing approaches (from different training paradigms) and the sub-performing Strategy 2. These visual examples complement the quantitative metrics by illustrating the practical differences in segmentation quality, particularly for challenging small lesions where the advantages of top-performing strategies are most obvious.
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Heatmap visualization of pairwise statistical significance comparing Dice scores between all the SwinUNETR segmentation strategies, ordered by descending mean Dice score for small lesions. The lower triangle displays p-values from paired t-tests, with white color scales indicating statistically significant differences (*p* < 0.05, marked as ‘1’) and red ones indicating non-significant differences (*p* ≥ 0.05, marked as ‘0’). Diagonal elements (gray) represent self-comparisons where *p* = 1. Strategies along rows and columns are identically ordered to facilitate comparison.</figcaption></figure>

### 4.5. Instance-wise analysis

Beyond voxel-based Dice metrics, evaluating performance at the lesion instance level provides critical information that more directly aligns with clinical workflow. Based on our previous analyses of segmentation performance, detection rates, and statistical significance, we now focus specifically on the previously identified top-performing strategies (1, 5, 7, and 9), while including Strategy 2 as a representative sub-performing approach for contrast and excluding those with self-supervised initialization. This selection allows us to understand how the best approaches differ in practical detection capabilities. In clinical practice, radiologists and oncologists assess individual lesions rather than voxel-level segmentation accuracy, making these instance-level metrics particularly relevant for real-world implementation. The instance performance evaluation, as shown in Table 6, highlights the effectiveness of different strategies in terms of FP, FN, and TP detection at the lesion level, as defined in Section 3.3, using connected component analysis. A detailed analysis reveals distinct trade-offs between strategies.

Strategy 1 achieves the lowest false negative rate (39 in total, 0.35 per examination in average), demonstrating excellent sensitivity in detecting lesions. However, this comes at the cost of a high false positive rate (1040 in total, 9.23 per examination in average). In contrast, Strategy 9, which employs multistage/cumulative fine-tuning across multiple datasets, dramatically reduces false positives (583 total, 5.21

per examination). Besides, the reduction in false positive instances from Strategy 2 (1035), the sub-performing approach, to Strategy 9 (583) represents a 43.7% improvement, which would significantly reduce the clinician's burden of reviewing false findings in a clinical context. Strategy 7 offers an effective middle ground with slightly better false negative performance (40 in total, 0.36 per examination in average) than Strategy 9, while achieving a substantial drop in false positives (625 in total, 5.53 per examination) compared to Strategy 1.

These metrics translate to superior precision for Strategy 9 (0.563) compared to Strategy 1 (0.422), while Strategy 1 achieves the highest recall (0.951) against Strategy 9 (0.940). The F1-score, which balances precision and recall, favors Strategy 9 (0.704) and Strategy 7 (0.695) over Strategy 1 (0.585), making them the most balanced approaches for clinical implementation despite its slightly higher false negative rates.

This lesion-level analysis complements the voxel-based Dice metrics by providing a more clinically relevant perspective on detection performance, highlighting that fine-tuning approaches (particularly Strategy 9) not only achieve accurate segmentation boundaries (like from scratch schemes), but also excel at correctly identifying lesions. This significant reduction in false positives suggests that either mono and multi-stage fine-tuning have a positive impact by leveraging diverse feature distributions across datasets and helping to model to learn more refined representations.
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Qualitative liver metastases segmentation results on MetaBrest test set ($D_3^{test}$) for SwinUNETR. Each row shows CT axial slices with overlapping segmentation maps: ground truth (GT), top-performing strategies (Strategy 1 [S1], Strategy 7 [S7], Strategy 9 [S9]), and sub-performing approach (Strategy 2 [S2]). The right column provides magnified regions highlighting critical segmentation differences, particularly for small lesions.</figcaption></figure>

4.6. *Impact of training data proportion*

To analyze the data efficiency of our approach, we evaluated the
segmentation performance across varying training data proportions as
illustrated in Fig. 8, focusing on Strategy 5. Strategy 5 was selected
for this analysis because it represents an optimal balance between seg-
mentation accuracy and data utilization, incorporating diverse datasets
($D_1$, $D_2$, $D_3^{train}$) without requiring complex pre-training schemes. This
makes it an ideal candidate to assess how performance scales with data
quantity, providing insights that are both practically applicable and
generalizable to real-world implementations.

Our findings reveal distinct convergence patterns across different
lesion size categories. For medium and large lesions, as well as the

aggregate metric Avg Global Dice, performance converges rapidly,
reaching optimal levels with only approximately 20% of the training
data. This suggests that the model can effectively learn to segment well-
defined metastases even with limited training examples. In contrast,
small lesion segmentation show a more gradual improvement curve,
requiring approximately 60% of the training data to reach convergence.
This delayed convergence pattern illustrates the inherent difficulty in
identifying and accurately delineating small lesions, which often have
less distinctive boundaries and lower contrast with surrounding liver
tissue. Furthermore, the wider standard deviation bands observed for
small lesions further emphasize the variability in performance for this
challenging subset.
<caption>**Table 6**</caption>

Comparison of instance detection performance across four of the top-performing SwinUNETR strategies. Strategy 2 is displayed as a sub-performing approach. The best and second-best results are shown in bold and underlined, respectively.

<table><thead><tr><th>Metric</th><th></th><th>Strategy 1</th><th>Strategy 2</th><th>Strategy 5</th><th>Strategy 7</th><th>Strategy 9</th></tr></thead><tbody><tr><td rowspan="2">False Positives</td><td>Total ↓</td><td>1040</td><td>1035</td><td>802</td><td>625</td><td>583</td></tr><tr><td>Avg/Exam ↓</td><td>9.29</td><td>9.24</td><td>7.16</td><td>5.58</td><td>5.21</td></tr><tr><td rowspan="2">False Negatives</td><td>Total ↓</td><td>39</td><td>95</td><td>48</td><td>40</td><td>48</td></tr><tr><td>Avg/Exam ↓</td><td>0.35</td><td>0.85</td><td>0.43</td><td>0.36</td><td>0.43</td></tr><tr><td rowspan="2">True Positives</td><td>Total ↑</td><td>761</td><td>705</td><td>752</td><td>760</td><td>752</td></tr><tr><td>Avg/Exam ↑</td><td>6.79</td><td>6.29</td><td>6.71</td><td>6.79</td><td>6.71</td></tr><tr><td>Precision ↑</td><td></td><td>0.422</td><td>0.405</td><td>0.484</td><td>0.549</td><td>0.563</td></tr><tr><td>Recall ↑</td><td></td><td><strong>0.951</strong></td><td>0.881</td><td>0.940</td><td><u>0.950</u></td><td>0.940</td></tr><tr><td>F1-score ↑</td><td></td><td>0.585</td><td>0.555</td><td>0.637</td><td><u>0.695</u></td><td><strong>0.704</strong></td></tr></tbody></table>

<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Data regime analysis showing scaling performance across the different lesion metrics evaluation categories for strategy 5 (trained on the set {$D_1, D_2, D_3^{train}$}) using SwinUNETR. Results curves show the performance when training on different percentages of the concatenated datasets. The shaded area represents the convergence area, defined by the highest observed performance with a tolerance interval of ±5%, vertical red lines indicating the first point of convergence for each category. The blue transparent band around each curve represents the standard deviation.</figcaption></figure>

These scaling results have important implications for clinical implementation, suggesting that while general segmentation capability can be achieved with less data requirements, accurate delineation of small lesions — which are critical for early detection or treatment planning — benefits significantly from larger, more diverse training datasets. This finding reinforces our overall observation that data diversity plays a crucial role in developing clinically viable segmentation tools for liver metastases.

## 5. Discussion

The results presented in this study highlight the importance of dataset diversity and task-specific training in deep segmentation of CRLM. While pre-trained Transformer-based models with self-supervised learning derived weights have demonstrated success in other medical imaging tasks (such as organ segmentation with limited 3D volumes available [24]), their limited effectiveness in this work suggests that CRLM segmentation requires specialized learning paradigms. This limited contribution from self-supervised initialization may be attributed to the domain gap between the general medical imaging features learned during pre-training (Table 9) and the specific characteristics of liver metastases. The pre-trained weights, while

effective for general anatomical structures, likely lack the specialized feature representations needed for detecting subtle differences in lesion appearance (Fig. 2), particularly for small metastases which represent the most challenging segmentation targets. Notably, strategies that tend to employ straightforward training methodologies but leverage diverse and heterogeneous data sources demonstrate superior performance compared to more complex transfer learning approaches. This finding has significant implications for practical implementation, suggesting that focusing on data quality, quantity and diversity may yield better results than sophisticated pre-training schemes, particularly when prioritizing small lesion detection and delineation.

The superior performance of strategies that were trained exclusively or partially on the heterogeneous LiTS dataset before application to the target CRLM dataset, suggests that exposure to a wide variety of liver lesion appearances during the learning phase enhances the model's ability to generalize to the specific characteristics of colorectal metastases. This aligns with the broader understanding in medical image analysis that model robustness often stems from data diversity rather than algorithmic complexity [26,27].

The consistency of learning patterns across three distinct architectures (U-Net, UNETR, SwinUNETR) demonstrates that our findings
<caption>**Table 7**</caption>

Characteristics of both training and test datasets. Median values and inter-quartile range (IQR) are displayed for each variable.

<table><caption>**Table 8**</caption><thead><tr><th></th><th>LiTS (D<sub>1</sub>)</th><th>metaRec (D<sub>2</sub>)</th><th>D<sub>3</sub><sup>train</sup></th><th>D<sub>3</sub><sup>test</sup></th></tr></thead><tbody><tr><td>In-plane resolution (mm)</td><td>0.76 (0.70, 0.85)</td><td>0.78 (0.72, 0.84)</td><td>0.73 (0.67, 0.80)</td><td>0.75 (0.69, 0.81)</td></tr><tr><td>Slice thickness (mm)</td><td>1.0 (0.8, 1.75)</td><td>5.0 (1.5, 5.0)</td><td>1.0 (1.0, 1.2)</td><td>1.2 (1.0, 1.2)</td></tr><tr><td>Volume size</td><td>512<sup>2</sup> × 172<br>(512<sup>2</sup> × 98,512<sup>2</sup> × 235)</td><td>512<sup>2</sup> × 55<br>(512<sup>2</sup> × 44,512<sup>2</sup> × 141)</td><td>512<sup>2</sup> × 556<br>(512<sup>2</sup> × 448,512<sup>2</sup> × 641)</td><td>512<sup>2</sup> × 532<br>(512<sup>2</sup> × 442,512<sup>2</sup> × 638)</td></tr><tr><td>Number of tumors</td><td>2 (1, 9)</td><td>2 (1, 3)</td><td>2 (1, 5)</td><td>3 (1, 9)</td></tr><tr><td>Tumor volume (mm<sup>3</sup>)</td><td>377<br>(75, 2182)</td><td>1487<br>(422, 7075)</td><td>552<br>(170, 2247)</td><td>1328<br>(460, 5235)</td></tr><tr><td>Liver volume (mm<sup>3</sup>)</td><td>(1631.29 ± 581.32) × 10<sup>3</sup><br>(1365.82 × 10<sup>3</sup>, 1893.42 × 10<sup>3</sup>)</td><td>(1597.54 ± 445.78) × 10<sup>3</sup><br>(1288.86 × 10<sup>3</sup>, 1804.35 × 10<sup>3</sup>)</td><td>(1529.66 ± 779.51) × 10<sup>3</sup><br>(1330.45 × 10<sup>3</sup>, 1902.45 × 10<sup>3</sup>)</td><td>(1789.32 ± 702.72) × 10<sup>3</sup><br>(1480.60 × 10<sup>3</sup>, 2170.88 × 10<sup>3</sup>)</td></tr></tbody></table>

Institutional sources for each dataset.

<table><thead><tr><th>Dataset</th><th>Institution</th><th>Nb. of sites</th><th>Acquisition machine</th></tr></thead><tbody><tr><td>LiTS (D<sub>1</sub>)</td><td>Rechts der Isar Hospital, TUM, Germany<br>Radboud University Medical Center, the Netherlands<br>Polytechnique Montreal and CHUM Research Center in Canada<br>IRCAD, France<br>Sheba Medical Center<br>Hebrew University of Jerusalem<br>Hadassah University</td><td>Unknown</td><td>Unknown</td></tr><tr><td>MetaRec (D<sub>2</sub>)</td><td>Memorial Sloan-Kettering Cancer Center, USA</td><td>1</td><td>GE Lightspeed VCT</td></tr><tr><td>MetaBrest (D<sub>3</sub>)</td><td>University Hospital of Brest, France</td><td>4</td><td>Siemens SOMATOM AS<br>Siemens SOMATOM AS +<br>Siemens SOMATOM Edge<br>Siemens Sensation Open<br>Toshiba Aquilion One<br>GE Optima CT<br>GE Revolution HD<br>GE LightSpeed VCT<br>GE BrightSpeed</td></tr></tbody></table>

reflect underlying general principles rather than architecture-specific phenomena. Nevertheless, the architectural comparison reveals that while conventional CNNs provide solid baseline performance for large lesions, Transformer-based approaches offer significant advantages for liver metastasis segmentation. In particular, the hierarchical design and efficient attention mechanisms of SwinUNETR appear particularly well-suited for the multi-scale nature of hepatic lesions, from large tumors to challenging small metastases. The broader landscape of medical image segmentation architectures continues to evolve, with recent works exploring multi-scale residual encoding approaches [28], medical image segmentation using parallel Transformers [29], and dual-path networks with rich information interactions [30]. Additionally, diffusion models have emerged as a promising paradigm for various medical imaging tasks [31]. This finding suggests that future development in medical image segmentation should consider both data diversity and architectural sophistication for optimal clinical performance.

This study yields several significant implications for clinical practice. Accurate segmentation of liver metastases, particularly small lesions that may be missed during routine radiological assessment, can significantly impact surgery planning, treatment planning, and monitoring, with reliable detection enabling earlier intervention and potentially improving patient outcomes. Our global Dice coefficient of ≈75% demonstrates competitive performance in liver tumor segmentation, aligning with the 0.70–0.78 range reported for top-performing methods in the LiTS benchmark [3], thereby supporting the clinical acceptability of our results. This performance level, combined with detection rates above 90% for small lesions, demonstrates the clinical viability of our approach. The high detection sensitivity achieved by some of the top-performing strategies demonstrates the potential for these algorithms to serve as effective computer-aided detection tools in clinical practice, reducing inter-observer variability and improving workflow efficiency [32–34]. The lower false positive rates achieved by strategies that integrate multiple datasets are particularly relevant for

<caption>**Table 9**</caption>

Summary of datasets used for pre-training through self-supervised learning.

<table><thead><tr><th>Dataset name</th><th>Data type</th><th>No. data</th><th>Source</th></tr></thead><tbody><tr><td>Aomic-1000</td><td>MRI</td><td>~2700</td><td>Open Neuro</td></tr><tr><td>QTIM</td><td>MRI</td><td>~1350</td><td>Open Neuro</td></tr><tr><td>Brats 2021</td><td>MRI</td><td>~5000</td><td>Brats Challenge</td></tr><tr><td>LUNA 16</td><td>CT</td><td>~900</td><td>LUNA Challenge</td></tr><tr><td>NLST</td><td>CT</td><td>~33500</td><td>TCIA</td></tr><tr><td>Duke Breast Cancer MR</td><td>MRI</td><td>~5000</td><td>TCIA</td></tr><tr><td>Acrin Breast MR</td><td>MRI</td><td>~7000</td><td>TCIA</td></tr><tr><td>Prostate MR</td><td>MRI</td><td>~900</td><td>TCIA</td></tr><tr><td>Stony Brook Chest CT</td><td>CT</td><td>~2700</td><td>TCIA</td></tr><tr><td>Upenn GBM</td><td>MRI</td><td>~2700</td><td>TCIA</td></tr><tr><td>LIDC</td><td>CT</td><td>~500</td><td>TCIA</td></tr><tr><td>Covid-19</td><td>CT</td><td>~700</td><td>TCIA</td></tr><tr><td>Colonography Trials</td><td>CT</td><td>~1600</td><td>TCIA</td></tr><tr><td>Head and Neck Carcinoma</td><td>PET-CT</td><td>~1300</td><td>TCIA</td></tr></tbody></table>

clinical deployment, as excessive false positives can lead to increased clinician workload, suggesting that comprehensive training approaches leveraging diverse data sources offer the most clinically viable solution for liver metastasis segmentation. However, false positive generation remains a concern, particularly in hepatic areas with lesion-mimicking structures [35].

## 6. Conclusion

This comprehensive evaluation of training strategies for liver metastasis segmentation offers several key insights into the optimal approaches for developing robust clinical tools. While domain-specific pre-training offers some benefits, our findings indicate that generic self-supervised weight initialization does not significantly improve performance in this specific clinical context. Such results emphasize the need for tailored learning approaches, with increased data diversity,
to improve segmentation precision, especially for small lesions that present the greatest clinical challenge.

Our study contributes to the on-going efforts to develop highly accurate automated lesion segmentation tools, with profound implications for improving patient outcomes in oncology. By providing a comprehensive evaluation of different training strategies, we aim to inform future research and development in this critical area of medical imaging. The superior performance of strategies employing diverse datasets underscores the importance of data quality and heterogeneity in achieving clinically viable segmentation performance.

The results also highlight that the delineation of small liver metastases remains challenging even with advanced deep learning architectures, requiring specialized training approaches that prioritize sensitivity to small-scale features. Future work should focus on enhancing the detection and segmentation of these clinically significant small lesions through targeted architectural modifications.

In conclusion, our findings suggest that for practical clinical implementation, approaches that maximize data diversity while employing straightforward training methodologies offer the most promising path toward automated liver metastasis segmentation. These can guide the development of computer-aided detection and segmentation tools that effectively support clinical decision-making in oncology practice.

## CRediT authorship contribution statement

**Marwan Abbas:** Writing – review & editing, Writing – original draft, Validation, Software, Methodology.
**Bogdan Badic:** Project administration, Investigation, Data curation, Conceptualization.
**Gustavo Andrade-Miranda:** Writing – review & editing, Supervision, Software, Methodology, Investigation, Conceptualization.
**Vincent Bourbonne:** Investigation, Conceptualization.
**Vincent Jaouen:** Writing – review & editing, Investigation, Conceptualization.
**Dimitris Visvikis:** Validation, Project administration, Methodology, Investigation, Conceptualization.
**Pierre-Henri Conze:** Writing – review & editing, Writing – original draft, Supervision, Software, Methodology, Investigation, Funding acquisition, Conceptualization.

## Compliance with ethical standards

This research study was conducted retrospectively using human subject data made available in open access [3,18]. It also contained data provided by University Hospital of Brest, France. Informed consents were obtained for all patients. Authors declare that they do not have conflicts of interest.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This work was partially funded by Ligue Contre le Cancer and France Life Imaging (grant ANR-11-INBS-0006). This project was provided with computing AI and storage resources by GENCI at IDRIS thanks to the grant 2024-AD011014506R1 number on the supercomputer Jean Zay's V100 partition.

## References

[1] A.W.C. Kow, Hepatic metastasis from colorectal cancer., J. Gastrointest. Oncol. 10 (6) (2019) 1274-1298.

[2] M. Donadon, D. Ribero, G. Morris-Stiff, E.K. Abdalla, J.-N. Vauthey, New paradigm in the management of liver-only metastases from colorectal cancer, Gastrointest. Cancer Res.: GCR 1 (1) (2007) 20.

[3] P. Bilic, P. Christ, H.B. Li, E. Vorontsov, A. Ben-Cohen, G. Kaissis, A. Szeskin, C. Jacobs, G.E.H. Mamani, G. Chartrand, et al., The liver tumor segmentation benchmark (liTS), Med. Image Anal. 84 (2023) 102680.

[4] P.-H. Conze, G. Andrade-Miranda, V.K. Singh, V. Jaouen, D. Visvikis, Current and emerging trends in medical image segmentation with deep learning, IEEE Trans. Radiat. Plasma Med. Sci. (2023).

[5] O. Ronneberger, P. Fischer, T. Brox, Unet: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention, 2015, pp. 234-241.

[6] E. Vorontsov, M. Cerny, P. Régnier, L. Di Jorio, C.J. Pal, R. Lapointe, F. Vandenbroucke-Menu, S. Turcotte, S. Kadoury, A. Tang, Deep learning for automated segmentation of liver lesions at CT in patients with colorectal cancer liver metastases, Radiol.: Artif. Intell. 1 (2) (2019) 180014.

[7] G. Andrade-Miranda, V. Jaouen, O. Tankyevych, C.C. Le Rest, D. Visvikis, P.-H. Conze, Multi-modal medical transformers: A meta-analysis for medical image segmentation in oncology, Comput. Med. Imaging Graph. (2023) 102308.

[8] J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A.L. Yuille, Y. Zhou, TransUNet: Transformers make strong encoders for medical image segmentation, 2021, arXiv preprint arXiv:2102.04306.

[9] A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H.R. Roth, D. Xu, Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images, in: International MICCAI Brainlesion Workshop, 2021.

[10] A. Vaswani, Attention is all you need, Adv. Neural Inf. Process. Syst. (2017).

[11] A. Hosny, C. Parmar, J. Quackenbush, L.H. Schwartz, H.J.W.L. Aerts, Artificial intelligence in radiology, Nat. Rev. Cancer 18 (8) (2018) 500-510, http://dx.doi.org/10.1038/s41568-018-0016-5.

[12] S.K. Mun, K.H. Wong, S.-C.B. Lo, Y. Li, S. Bayarsaikhan, Artificial intelligence for the future radiology diagnostic service, Front. Mol. Biosci. 7 (2021) http://dx.doi.org/10.3389/fmolb.2020.614258.

[13] R. Zheng, L. Wang, C. Wang, X. Yu, W. Chen, Y. Li, W. Li, F. Yan, H. Wang, R. Li, Feasibility of automatic detection of small hepatocellular carcinoma (≤2 cm) in cirrhotic liver based on pattern matching and deep learning, Phys. Med. Biol. 66 (8) (2021) 085014.

[14] H. Guan, M. Liu, Domain adaptation for medical image analysis: a survey, IEEE Trans. Biomed. Eng. 69 (3) (2021) 1173-1185.

[15] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, Adv. Neural Inf. Process. Syst. 27 (2014).

[16] Y. Ganin, V. Lempitsky, Unsupervised domain adaptation by backpropagation, in: International Conference on Machine Learning, 2015, pp. 1180-1189.

[17] E. Goceri, Medical image data augmentation: techniques, comparisons and interpretations, Artif. Intell. Rev. 56 (11) (2023) 12561-12605, http://dx.doi.org/10.1007/s10462-023-10453-z.

[18] A. Simpson, J. Peoples, J. Creasy, et al., Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases, Sci. Data 11 (2024) 172, http://dx.doi.org/10.1038/s41597-024-02981-2.

[19] A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet, J.-C. Fillion-Robin, S. Pujol, C. Bauer, D. Jennings, F. Fennessy, M. Sonka, J. Buatti, S.R. Aylward, J.V. Miller, S. Pieper, R. Kikinis, 3D slicer as an image computing platform for the quantitative imaging network, Magn. Reson. Imaging 30 (9) (2012) 1323-1341, http://dx.doi.org/10.1016/j.mri.2012.05.001.

[20] E.A. Eisenhauer, P. Therasse, J. Bogaerts, L.H. Schwartz, D. Sargent, R. Ford, J. Dancey, S. Arbuck, S. Gwyther, M. Mooney, et al., New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1), Eur. J. Cancer 45 (2) (2009) 228-247.

[21] A. Hatamizadeh, D. Yang, H.R. Roth, D. Xu, UNETR: Transformers for 3D medical image segmentation, 2022 IEEE/CVF Winter Conf. Appl. Comput. Vision (WACV) (2021) 1748-1758, URL: https://api.semanticscholar.org/CorpusID:232290634.

[22] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16×16 words: Transformers for image recognition at scale, 2020, ArXiv.

[23] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin transformer: Hierarchical vision transformer using shifted windows, in: IEEE/CVF International Conference on Computer Vision, 2021.

[24] Y. Tang, D. Yang, W. Li, H.R. Roth, B. Landman, D. Xu, V. Nath, A. Hatamizadeh, Self-supervised pre-training of swin transformers for 3D medical image analysis, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20730-20740.

[25] M. Rottmann, K. Maag, R. Chan, F. Hüger, P. Schlicht, H. Gottschalk, Detection of false positive and false negative samples in semantic segmentation, in: Design, Automation & Test, 2020, pp. 1351-1356.
[26] J. Wasserthal, H.-C. Breit, M.T. Meyer, M. Pradella, D. Hinck, A.W. Sauter, T. Heye, D.T. Boll, J. Cyriac, S. Yang, M. Bach, M. Segeroth, TotalSegmentator: Robust segmentation of 104 anatomic structures in ct images, Radiol.: Artif. Intell. 5 (5) (2023) http://dx.doi.org/10.1148/ryai.230024.

[27] F. Isensee, T. Wald, C. Ulrich, M. Baumgartner, S. Roy, K. Maier-Hein, P.F. Jaeger, Nnu-net revisited: A call for rigorous validation in 3D medical image segmentation, 2024, arXiv:2404.09556. URL: https://arxiv.org/abs/2404.09556.

[28] D. Dai, C. Dong, S. Xu, et al., Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation, Med. Image Anal. 75 (2022) 102293.

[29] Q. Yan, S. Liu, S. Xu, C. Dong, Z. Li, J.Q. Shi, Y. Zhang, D. Dai, 3D medical image segmentation using parallel transformers, Pattern Recognit. (2023).

[30] D. Dai, C. Dong, Q. Yan, Y. Sun, C. Zhang, Z. Li, S. Xu, I'u-net: A dual-path U-net with rich information interaction for medical image segmentation, Med. Image Anal. (2024) 103241.

[31] A. Kazerouni, E.K. Aghdam, M. Heidari, R. Azad, M. Fayyaz, I. Hacihaliloglu, D. Merhof, Diffusion models in medical imaging: A comprehensive survey, Med. Image Anal. 88 (2023) 102846.

[32] M. Khalifa, M. Albadawy, AI in diagnostic imaging: Revolutionising accuracy and efficiency, Comput. Methods Programs Biomed. 5 (2024) 100146, http://dx.doi.org/10.1016/j.cmpbup.2024.100146.

[33] I. Chouvarda, S. Colantonio, A.S.C. Verde, A. Jimenez-Pastor, L. Cerdá-Alberich, Y. Metz, L. Zacharias, S. Nabhani-Gebara, M. Bobowicz, G. Tsakou, K. Lekadir, M. Tsiknakis, L. Martí-Bonmati, N. Papanikolaou, Differences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!, Eur. Radiol. Exp. 9 (1) (2025) http://dx.doi.org/10.1186/s41747-024-00543-0.

[34] K. Lekadir, A.F. Frangi, A.R. Porras, B. Glocker, C. Cintas, C.P. Langlotz, E. Weicken, F.W. Asselbergs, F. Prior, G.S. Collins, G. Kaissis, G. Tsakou, I. Buvat, J. Kalpathy-Cramer, J. Mongan, J.A. Schnabel, K. Kushibar, K. Riklund, K. Marias, L.M. Amugongo, L.A. Fromont, L. Maier-Hein, L. Cerdá-Alberich, L. Martí-Bonmatí, M.J. Cardoso, M. Bobowicz, M. Shabani, M. Tsiknakis, M.A. Zuluaga, M.-C. Fritzsche, M. Camacho, M.G. Linguraru, M. Wenzel, M. De Bruijne, M.G. Tolsgaard, M. Goisauf, M. Cano Abadía, N. Papanikolaou, N. Lazrak, O. Pujol, R. Osuala, S. Napel, S. Colantonio, S. Joshi, S. Klein, S. Aussó, W.A. Rogers, Z. Salahuddin, M. Starmans, FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare, BMJ (2025) http://dx.doi.org/10.1136/bmj-2024-081554.

[35] N. Stanietzky, A.E. Salem, K.M. Elsayes, M. Rezvani, K. Fraivillig, U. Salem, S. Klimkowski, M. Diab, S. Naik, C.O. Menias, A.M. Shaaban, Tumor-like conditions that mimic liver tumors, Diagn. Interv. Radiol. (2024) http://dx.doi.org/10.4274/dir.2024.242826.