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# SR-MedTS: A Semantically-Robust Synergistic Framework for multi-modal Medical Image Translation and Segmentation

Mingbo Su, Yi Zhang, Mounir Kaaniche, Congcong Wang, Meng Zhao

► To cite this version:

Mingbo Su, Yi Zhang, Mounir Kaaniche, Congcong Wang, Meng Zhao. SR-MedTS: A Semantically-Robust Synergistic Framework for multi-modal Medical Image Translation and Segmentation. BIBM 2025 - International Conference on Bioinformatics and Biomedicine, Dec 2025, Wuhan, China. hal-05440309

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https://hal.science/hal-05440309v1

Submitted on 4 Jan 2026

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
SR-MedTS: A Semantically-Robust Synergistic
Framework for multi-modal Medical Image
Translation and Segmentation

Mingbo Su¹, Yi Zhang¹, Mounir Kaaniche²,³, Congcong Wang¹, and Meng Zhao¹,*,

¹School of Computer Science and Engineering (ERC-LIS), Tianjin University of Technology, Tianjin 300384, China

²Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430, Villetaneuse, France

³Université Paris-Saclay, CentraleSupélec, CVN-OPIS, Inria, 91190 Gif-sur-Yvette, France

mingbosusmb@163.com, wazy1124@stud.tjut.edu.cn,

mounir.kaaniche@univ-paris13.fr, congcong_wang@yeah.net, zh_m@tju.edu.cn

***Abstract***—Multi-modal medical image translation and seg-
mentation are essential for achieving accurate diagnosis and
treatment. However, existing methods often suffer from seman-
tic shifts during modality translation, leading to issues such
as vascular discontinuity and anatomical deformation, which
further degrade downstream segmentation performance. To ad-
dress these challenges, we propose a Semantic-Robust multi-
modal medical image Translation and Segmentation framework
(SR-MedTS). In this respect, we design an end-to-end dual-
stream architecture composed of a generator, discriminator and
semantic robustness module. Moreover, we propose a channel-
spatial attention mechanism embedded in the skip connections
between the encoder and decoder of the segmentation generator
network to enhance boundary recognition. Finally, a multi-
loss function is defined to optimize the overall architecture.
Extensive experiments on a multi-modal abdominal dataset and
a six-site prostate dataset demonstrate that SR-MedTS signif-
icantly improves cross-modal segmentation performance using
only 10% of annotated data, showing strong potential in low-
resource medical imaging scenarios. Our code is available at
https://github.com/susu337/SR-MedTS.

***Index Terms***—multi-modal image translation, medical image segmentation, semantic robustness, contrastive learning, semi-supervised learning.

I. INTRODUCTION

One core challenge in cross-modal medical image trans-
lation lies in preserving anatomical structures during the
modality transformation. Although the goal of translation
models is to synthesize missing modalities (e.g., generating CT
from MRI), they often introduce structural distortions, such as
blurred organ boundaries, broken vessels, or shape deforma-
tion. Such distortions are often caused by modality registration
errors, domain distribution shifts, and image noise [1], which
induce semantic shifts in the latent space—manifesting as
blurred organ boundaries and topological inaccuracies in the
translated images (as shown in Fig. 1).

In clinical practice, multi-modal images such as CT and
MRI can provide complementary diagnostic information.
However, missing modalities are common due to contraindi-
cations or equipment limitations, forcing clinicians to rely
on a single modality, which may cause diagnostic ambiguity

when key structures are visible only in specific modalities.
Therefore, cross-modal image translation has emerged as a
promising solution. By converting available modalities into
missing ones, it is possible to enhance the performance of
downstream tasks (e.g., segmentation) even with only single-
modality input, without relying on explicit multi-modal fusion.

In this respect, existing translation methods, based on Gen-
erative Adversarial Networks (GANs) or diffusion models,
have been developed [3], [4]. However, despite their great
potential, they suffer from fundamental limitations. The syn-
thesized images often exhibit anatomical topological errors
(e.g., vessel discontinuity rate exceeding 15%) [5], noise
interference during the encoding process may obscure critical
anatomical structures [3], and translation errors can propagate

<figure><img src="image_3.png" /><figcaption>Fig. 1. Structural distortion and segmentation inconsistency in modality translation. The first row shows the modality translation results from T1in to CT using different methods. The leftmost column presents the original T1in image. The second and third columns show translated CT images generated by SMSUT [2] and our method, respectively. The second row shows the corresponding segmentation maps: ground truth (GT), prediction by SMSUT [2], and prediction by our method. The left kidney region (highlighted by a red bounding box) shows that SMSUT produces blurred anatomical boundaries and misses the right kidney label, while our method better preserves structural fidelity and segmentation accuracy.</figcaption></figure>
to downstream segmentation tasks, leading to significant performance degradation [4]. The root cause of these problems lies in the reliance of existing methods on passive alignment strategies, without explicitly introducing robustness constraints on anatomical structures.

While prior studies have explored improvements from the perspectives of image-level consistency (e.g., CycleGAN [6], SynSeg-Net [7]), feature-level alignment (e.g., PatchNCE [8]), and segmentation-guided supervision (e.g., SegAN [9]), three major limitations remain: contrastive learning approaches (e.g., PatchNCE [8]) only minimize feature distance, failing to actively enhance noise robustness; input-level noise injection methods damage anatomy-sensitive regions (e.g., organ boundaries [10]); and applying uniform semantic constraints across shallow and deep layers overlooks differences in semantic abstraction [11].

Radiologists can accurately recognize anatomical structures even with partial noise, thanks to their hierarchical understanding—from global organ shape to local boundary details [12]. This reflects an ability to extract anatomical invariance. Based on this insight, we focus on the following question: Can we actively inject controllable perturbations into the feature space to guide the network in learning anatomical invariance via self-supervised alignment?

Therefore, we propose a novel Semantic-Robust multi-modal Medical Image Translation and Segmentation framework (SR-MedTS), which achieves mutual enhancement of translation and segmentation through an end-to-end optimized dual-stream architecture. Our main contributions are as follows.

* **Design of a novel SR-MedTS framework:** We design an end-to-end dual-stream architecture consisting of a Generator, Discriminator, and a Semantic Robustness Module (SRM). This design enables translation and segmentation to benefit from each other while maintaining anatomical consistency.

* **Integration of a Dual-Attention (DA) Module:** We incorporate a channel-spatial attention module into the segmentation generator network to enhance boundary recognition and fine-grained localization.

* **Design of a multi-loss optimization strategy:** To improve semantic robustness and structural fidelity, we propose a combination of dynamic and multi-scale constraints to optimize the developed SR-MedTS architecture.

## II. RELATED WORK

### A. Modality Translation Methods for Medical Images

CycleGAN [6] introduced cycle-consistency loss for unsupervised modality translation, but often caused anatomical distortion in unpaired medical scenarios. Conditional GAN extensions [13] improved synthesis quality but relied on pixel-level supervision, limiting structural preservation. Diffusion-based models [14] generated realistic textures but lacked anatomical consistency.

To enhance semantic consistency, contrastive methods such as PatchNCE [8] performed patch-level feature alignment, yet remained sensitive to cross-modal noise. MDNet [15] attempted modality-content disentanglement but showed limited generalization. In contrast, our method introduces structure-aware perturbations and self-supervised alignment to achieve robust semantic representation across modalities.

### B. Feature Robustness Modeling in Medical Imaging

Feature-level robustness is crucial for generalization in medical image analysis. Noise2Noise [16] leveraged paired noisy data but required strict acquisition protocols. Input-level perturbation methods lacked anatomical awareness, while adversarial feature-space perturbations like TIRL [17] improved classification robustness but failed in structure-sensitive tasks.

Beyond spatial-domain perturbations, frequency-aligned distillation methods such as SDKD [18] enhance model robustness by aligning teacher-student representations in the frequency spectrum. Inspired by these works, our SR-MedTS introduces a Dynamic Layer-wise Semantic Robustness (DLSR) mechanism that adaptively scales perturbations according to feature sensitivity, preserving anatomical fidelity under semantic variation.

### C. Joint Translation and Segmentation Frameworks in Medical Imaging

Recent works such as CMC [19] and DCA [20] enhanced multi-modal segmentation via cross-modality attention, yet lacked explicit translation modules. AMMKD [21] improved task coordination via shared backbones but increased complexity. SMSUT [2] unified translation and segmentation under a conditional generative paradigm, while AMMKD [22] adopted a teacher-student scheme for modality robustness.

Different from these, our SR-MedTS employs an end-to-end dual-stream design that jointly optimizes translation and segmentation with structure-aware perturbations and self-supervised alignment, achieving semantically stable and anatomically consistent results. Additionally, a multi-scale perceptual reconstruction loss ensures translation fidelity and boosts segmentation accuracy, enabling efficient and collaborative modeling in real-world multi-modal clinical scenarios.

## III. PROPOSED METHOD

### A. Overall Architecture

As shown in Fig. 2, SR-MedTS is an end-to-end dual-stream framework for multi-modal medical image translation and segmentation. Given a source image $I_o \in \mathbb{R}^{H \times W}$ from modality $o$, the generator $G$ produces a translated target-modality image $I_t$ and a segmentation output $P_o$. The translation preserves semantic content while matching the appearance of the target modality (see Section III-B).

To ensure semantic consistency during modality translation, we introduce a semantic robustness-driven mechanism, which integrates dynamic hierarchical feature perturbation and dynamic channel adaptation via contrastive learning. The overall architecture is optimized through an appropriate multi-loss function as described in Section III-C.
<figure><img src="image_4.png" /><figcaption>Fig. 2. Illustration of the SR-MedTS framework. During the training phase, both labeled and unlabeled data are used as input. The blue arrows indicate the segmentation branch, the green arrows denote the modality translation branch, and the red arrows represent the loss functions involved in training. In the inference phase, the generator G generates an image $I_t$ in the target modality while preserving the semantic information of the source image $I_o$, and simultaneously outputs the predicted segmentation map $P_o$ of the source image.</figcaption></figure>

## B. Architecture Components

1) **Generator**: The generator follows a U-Net-based encoder-decoder with two branches: a modality translation sub-network ($G_{tran}$) and a segmentation sub-network ($G_{seg}$), sharing semantic features through Shared Convolutional Layers (SCL). It outputs a translated image $I_t = G_{tran}(I_o + ModVector_{ot})$ and a segmentation map $P_o = G_{seg}(I_o)$, where ModVector_{ot} = $e_t - e_o$ represents the learned modality difference embedding. For reconstruction, the inverse vector ModVector_{to} = $e_o - e_t$ allows the recovery of the original image $\tilde{I}_o = G_{tran}(I_t + ModVector_{to})$.

2) **Discriminator**: The discriminator adopts a PatchGAN structure with the WGAN-GP loss [23] for stable training. Unlike standard discriminators, it includes an auxiliary modality-classification branch, enabling it to distinguish not only real vs. fake images but also to verify modality conformity.

3) **Dual Attention Mechanism**: To enhance representation and boundary localization, we integrate a Dual Attention (DA) module into the skip connection of $G_{seg}$. It consists of a Channel Attention Module (CAM) and a Position Attention Module (PAM).

**CAM**: CAM models inter-channel relationships by capturing dependencies among feature channels. Given an input feature map $\mathbf{F} \in \mathbb{R}^{C \times H \times W}$, CAM computes a channel attention matrix $\mathbf{A}_{\text{cam}} \in \mathbb{R}^{C \times C}$ given by

$$ \mathbf{A}_{\text{cam}}(i, j) = \frac{\exp(\mathbf{f}_i^{\top} \mathbf{f}_j)}{\sum_{k=1}^{C} \exp(\mathbf{f}_i^{\top} \mathbf{f}_k)}, \quad (1) $$

where $\mathbf{f}_i$ and $\mathbf{f}_j$ are global descriptors of channels $i$ and $j$, yielding the refined features: $\mathbf{F}_1 = \mathbf{A}_{\text{cam}}\mathbf{F}$.

**PAM**: PAM models long-range dependencies across spatial positions, with the attention matrix $\mathbf{A}_{\text{pam}} \in \mathbb{R}^{(H \times W) \times (H \times W)}$ defined as:

$$ \mathbf{A}_{\text{pam}}(p, q) = \frac{\exp(\mathbf{g}_p^{\top} \mathbf{g}_q)}{\sum_{k=1}^{H \times W} \exp(\mathbf{g}_p^{\top} \mathbf{g}_k)}, \quad (2) $$

where $\mathbf{g}_p$ and $\mathbf{g}_q$ denote feature vectors at spatial positions $p$ and $q$, respectively, yielding the refined feature $\mathbf{F}_2 = \mathbf{A}_{\text{pam}}\mathbf{F}$.

**Integration in the Generator network**: The DA module is placed in the final skip connection of $G_{seg}$. The CAM and PAM outputs are fused to produce the final feature:

$$ \mathbf{F}_{\text{da}} = \sigma(\text{Norm}(\text{Conv}_{1 \times 1}(\mathbf{F}_1 + \mathbf{F}_2))), \quad (3) $$

where $\sigma$ is a non-linear activation and Norm denotes instance normalization.

4) **Semantic Robustness Module (SRM)**: To enhance the semantic consistency of anatomical structures during modality translation, we propose a Semantic Robustness Module (SRM) that guides the generator to learn modality-invariant structural representations by simulated multi-scale perturbations, thereby improving structural fidelity and robustness. The core component of SRM, shown in Fig. 2, is the Dynamic Hierarchical Feature Perturbation.

**Dynamic Hierarchical Feature Perturbation**: To simulate realistic clinical noise such as motion or metal artifacts, structure-aware stochastic perturbations are injected into
encoder features at different layers. Given a feature map $F_l \in \mathbb{R}^{B \times C_l \times H_l \times W_l}$, the perturbed feature is defined as $\tilde{F}_l = F_l + n_l$, where $n_l = \epsilon \odot R_l$, $\epsilon \sim \mathcal{N}(0, \sigma^2)$ is L2-normalized Gaussian noise, and $R_l \sim \text{Uniform}[0, 1]^{B \times 1 \times H_l \times W_l}$ is a spatially random scaling factor.

At each iteration, SRM injects perturbations into a randomly selected encoder layer, adaptively scaling the strength per patch. This prevents over-regularization, enhances robustness to semantic variations across scales, and improves structural consistency, forming the basis for the semantic constraints and robustness-aware losses discussed in the next subsection.

### C. Learning Strategy and Loss Function

Our model is trained with a combination of task-specific losses that jointly ensure high-quality translation, modality alignment, semantic consistency, and structural robustness. The total objective comprises adversarial, modality classification, supervised segmentation, segmentation consistency, multi-scale perceptual reconstruction, contrastive, and semantic robustness losses.

**Adversarial Loss:** To improve image realism and sharpness, we employ adversarial training with gradient penalty, where the generator $G$ fools the discriminator $D$ distinguishing real from synthesized images:

$$ \mathcal{L}_{\text{adv}}^{G} = -\mathbb{E}[D_{\text{src}}(I_t)], \quad (4) $$

$$ \mathcal{L}_{\text{adv}}^{D} = -\mathbb{E}[D_{\text{src}}(I_o)] + \mathbb{E}[D_{\text{src}}(I_t)] + \lambda_{\text{gp}}\mathbb{E}\left[\left(\left\|\nabla D_{\text{src}}(\tilde{I})\right\|_2 - 1\right)^2\right], \quad (5) $$

$$ \mathcal{L}_{\text{adv}} = \lambda_{\text{adv}} \mathcal{L}_{\text{adv}}^{G} + \mathcal{L}_{\text{adv}}^{D} \quad (6) $$

where $\tilde{I}$ denotes an interpolated image between $I_o$ and $I_t$, $D_{\text{src}}(\cdot)$ is the source branch of the PatchGAN discriminator, and $\lambda_{\text{adv}}$ balances the generator's adversarial loss.

**Modality Classification Loss:** To ensure the translated image aligns with the target modality, we add a classification branch in the discriminator to supervise the modality type:

$$ \mathcal{L}_{\text{cls}}^{G} = -\mathbb{E}[M_t \log D_{\text{cls}}(M_t | I_t)], \quad (7) $$

$$ \mathcal{L}_{\text{cls}}^{D} = -\mathbb{E}[M_o \log D_{\text{cls}}(M_o | I_o)] + \mathbb{E}[M_o \log D_{\text{cls}}(M_t | I_t)], \quad (8) $$

$$ \mathcal{L}_{\text{cls}} = \lambda_{\text{cls}} \mathcal{L}_{\text{cls}}^{G} + \lambda_{\text{cls}} \mathcal{L}_{\text{cls}}^{D} \quad (9) $$

where $M_o$ and $M_t$ are the modality labels of $I_o$ and $I_t$, respectively, and $\lambda_{\text{cls}}$ balances the classification losses of $G$ and $D$.

**Supervised Segmentation Loss and Segmentation Consistency Loss:** The supervised loss guides segmentation on labeled data:

$$ \mathcal{L}_{\text{seg}} = \mathbb{E} \left[ - \left( Y_o^{(l)} \log P_o^{(l)} + \frac{2 P_o^{(l)} Y_o^{(l)}}{\| P_o^{(l)} \|_1 + \| Y_o^{(l)} \|_1} \right) \right] \quad (10) $$

where $Y_o^{(l)}$ and $P_o^{(l)}$ denote the ground-truth mask and predicted probability map of labeled data, respectively. The loss combines cross-entropy and soft Dice loss to improve segmentation on annotated samples. Here, $\| \cdot \|_1$ denotes the

$l_1$ norm. To ensure structural consistency across domains, we introduce the segmentation consistency loss:

$$ \mathcal{L}_{\text{cons}} = \mathbb{E} \left[ - \left( P_o \log P_t + \frac{2 P_o P_t}{\| P_o \|_1 + \| P_t \|_1} \right) \right] \quad (11) $$

where $P_o$ and $P_t$ denote the predicted segmentation maps of the source and translated images, respectively. This loss enforces structural consistency between them.

We apply a dynamic exponential growth strategy for the weighting factor $\lambda_{\text{cons}}$:

$$ \lambda_{\text{cons}} = \lambda_{\text{max}} \exp \left[ -5 \left( 1 - \frac{T}{T_{\text{max}}} \right)^2 \right] \quad (12) $$

where $\lambda_{\text{max}}$ is the maximum weight, $T$ is the current training epoch, and $T_{\text{max}}$ is the total number of training epochs.

**Multi-Scale Perceptual Reconstruction (MPR) Loss:** To enforce reversibility of modality translation, we employ pixel-wise cycle consistency:

$$ \mathcal{L}_{\text{rec}} = \mathbb{E} \left[ \| I_o - \tilde{I}_o \|_1 \right], \quad (13) $$

and a Learned Perceptual Image Patch Similarity (LPIPS) loss computed via VGG16 features:

$$ \mathcal{L}_{\text{lpips}} = \sum_{k=1}^{5} \lambda_k \| \phi_k(I_o) - \phi_k(\tilde{I}_o) \|_2^2 \quad (14) $$

where $\phi_k(\cdot)$ denotes the $k$-th layer feature map.

**Contrastive Loss on Shared Convolutional Layers:** To align content features between segmentation and translation pathways, we use a PatchNCE-based contrastive loss [8] on shared convolutional layers (SCL):

$$ \mathcal{L}_{\text{nce}} = \mathbb{E}[\mathcal{L}_{\text{PatchNCE}}(G, H, I_o) + \mathcal{L}_{\text{PatchNCE}}(G, H, I_t)], \quad (15) $$

where $H$ is a projection head MLP and $\mathcal{L}_{\text{PatchNCE}}$ is given by

$$ \mathcal{L}_{\text{PatchNCE}}(G, H, I) = - \log \frac{\exp(\text{sim}(\mathbf{z}_q, \mathbf{z}_k^+)/\tau)}{\sum_{\mathbf{z}_k \in \Omega} \exp(\text{sim}(\mathbf{z}_q, \mathbf{z}_k)/\tau)}, \quad (16) $$

Here, $\mathbf{z}_q$ and $\mathbf{z}_k^+$ are query and positive key features from the same spatial location of image $I$ after processing by $G$ and $H$; $\Omega$ is the set of negative features from other locations, $\text{sim}(\cdot, \cdot)$ denotes cosine similarity, and $\tau$ is the temperature parameter.

**Semantic Robustness Loss from SRM:** To promote semantic invariance under perturbation, we adopt a contrastive learning strategy based on our SRM module. Given perturbed features $\tilde{F}_l$, we generate a pseudo image $I_l^p$ and re-encode it to obtain $F_l^p$. Both $F_l$ and $F_l^p$ are passed through a shared projection head MLP, yielding

$$ f_k = \text{MLP}(F_l), \quad f_q = \text{MLP}(F_l^p) \quad (17) $$

Inspired by the InfoNCE objective [24], we evaluate the Dynamic Layer-wise Semantic Robustness Loss (DLSR) using a contrastive formulation:

$$ \mathcal{L}_{\text{DLSR}} = \mathbb{E} \left[ \frac{1}{\|n_l\|_2} \cdot \|f_k - f_q\|_2 \right] \quad (18) $$
<figure><img src="image_5.png" /><figcaption>Fig. 3. Qualitative results of segmentation and modality translation on the abdominal dataset with 10% annotation ratio. The figure includes the source image Io, ground truth (GT), segmentation results of comparison methods and our method, as well as translated images in four modalities (CT, T1in, T1out, T2). Segmentation masks highlight four organs: liver (red), right kidney (green), left kidney (blue), and pancreas (yellow).</figcaption></figure>

The inverse scaling factor $1/||n_l||_2$ enables noise-adaptive regularization. For instance, larger perturbations lead to smaller penalization, allowing deeper layers to tolerate semantic shifts while ensuring shallow layers to retain detailed structure.

**Total Loss:** The aforementioned loss functions are combined to obtain the final loss used to optimize the proposed SR-MedTS architecture:

$$ \min_G \max_D \mathcal{L}_{\text{adv}} + \mathcal{L}_{\text{cls}} + \lambda_{\text{seg}} \mathcal{L}_{\text{seg}} + \lambda_{\text{cons}} \mathcal{L}_{\text{cons}} \\ + \lambda_{\text{rec}} \mathcal{L}_{\text{rec}} + \lambda_{\text{lpips}} \mathcal{L}_{\text{lpips}} \\ + \lambda_{\text{DLSR}} \mathcal{L}_{\text{DLSR}} + \lambda_{\text{nce}} \mathcal{L}_{\text{nce}} \quad (19) $$

where $\lambda_{\text{seg}}$, $\lambda_{\text{cons}}$, $\lambda_{\text{rec}}$, $\lambda_{\text{lpips}}$, $\lambda_{\text{DLSR}}$, $\lambda_{\text{nce}}$ represent the weighting hyperparameters for each loss term.

# IV. EXPERIMENTS

## A. Datasets and Evaluation Metrics

We conduct experiments on two publicly available multi-modal medical image segmentation datasets: the multi-modal abdominal dataset [25], [26] and the multi-center prostate MRI dataset [27]. The abdominal dataset contains 90 3D cases with four imaging modalities (CT, T1-in-phase, T1-out-phase, and T2w-SPIR), each case consisting of approximately 36 slices. The CT data are from the MICCAI 2015 Beyond the Cranial Vault challenge [26], while the MRI data are from the ISBI 2019 CHAOS challenge [25]. The prostate MRI dataset includes 116 cases from six centers, with each case containing around 33 slices. To ensure fair cross-modal evaluation, data were split at the patient level so that no patient appeared in both training and test sets. As CT and MR images were unpaired and originated from different institutions, we balanced their distributions via random sampling to mitigate institution-specific biases.

The choice of appropriate evaluation metrics is critical for quantitatively assessing translation performance and reducing semantic inversion. We employ the Dice coefficient, Average Symmetric Surface Distance (ASSD), and Hausdorff Distance (HD) to evaluate the generated segmentation labels.

## B. Comparison Methods

We compare our method with several state-of-the-art semi-supervised segmentation approaches, including CPS [28], coraNet [29], PLGC [30], M3L [31], and SMSUT [2]. These methods involve strategies such as cross pseudo supervision, uncertainty modeling, contrastive learning, and multi-modal learning to leverage unlabeled data.

## C. Implementation Details

We split the datasets into training and testing sets: the abdominal dataset is divided (1:1), and the prostate dataset is split (2:1). To evaluate label efficiency, we test the model under six labeled ratios: {10%, 30%, 50%, 70%, 90%, 100%}. All images are resized to 256×256.

The training hyperparameters are set as follows: $\lambda_{\text{adv}} = \lambda_{\text{cls}} = \lambda_{\text{nce}} = 1$, $\lambda_{\text{rec}} = \lambda_{\text{seg}} = \lambda_{\text{gp}} = \lambda_{\text{max}} = 10$, $\lambda_{\text{DLSR}} = 0.001$ and $\lambda_{\text{lpips}} = 0.2$. We train the architecture using 200 epochs with a batch size of 8. To stabilize early training, $\lambda_{\text{cons}}$ is set to 0 during the first 1000 iterations, and $\lambda_{\text{DLSR}}$ is introduced only after 25% of training. All experiments are carried out using PyTorch on an NVIDIA GeForce RTX 4090 GPU.

## D. Experimental Results and Analysis

Tables I and II show the performance of SR-MedTS and state-of-the-art methods on two multi-modal segmentation datasets. SR-MedTS consistently achieves the best results across all annotation ratios, with notable gains under low-label settings (e.g., 10% and 30%).

At 10% annotation on the abdominal dataset, SR-MedTS reaches a Dice score of 83.95%, clearly outperforming others. On the prostate dataset, the HD decreases to 6.25, substantially improving over 13.02 from CoraNet. These results confirm that SR-MedTS effectively models cross-modal mappings while preserving anatomical structures, enhancing both segmentation accuracy and structural fidelity.

As shown in Fig. 3, under the 10% label setting, most methods fail to segment the left kidney or exhibit semantic
<table><caption>TABLE I
COMPARISON OF SEGMENTATION PERFORMANCE UNDER DIFFERENT ANNOTATION RATIOS ON THE MULTI-MODAL ABDOMINAL DATASET. “+GAUSSIAN” AND “+SALT_PEPPER” INDICATE NOISY INPUT EVALUATION.</caption><thead><tr><th rowspan="2">Ratio</th><th colspan="3">CPS [28]</th><th colspan="3">CoraNet [29]</th><th colspan="3">PLGCL [30]</th><th colspan="3">M3L [31]</th><th colspan="3">SMSUT [2]</th><th colspan="3">SR-MedTS (Ours)</th></tr><tr><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th></tr></thead><tbody><tr><td>10%</td><td>76.23</td><td>3.66</td><td>10.35</td><td>56.39</td><td>9.06</td><td>21.82</td><td>77.84</td><td>4.82</td><td>12.15</td><td>73.15</td><td>6.20</td><td>15.90</td><td>83.16</td><td>2.98</td><td>6.31</td><td><b>83.95</b></td><td><b>2.72</b></td><td><b>6.18</b></td></tr><tr><td>30%</td><td>82.68</td><td>2.84</td><td>7.51</td><td>80.16</td><td>3.48</td><td>8.55</td><td>81.22</td><td>3.75</td><td>9.04</td><td>78.93</td><td>4.85</td><td>10.55</td><td>85.11</td><td>2.71</td><td>6.36</td><td><b>86.33</b></td><td><b>2.27</b></td><td><b>3.92</b></td></tr><tr><td>50%</td><td>83.79</td><td>2.60</td><td>4.89</td><td>82.99</td><td>2.94</td><td>5.88</td><td>84.50</td><td>3.10</td><td>6.95</td><td>82.05</td><td>3.65</td><td>7.80</td><td>85.93</td><td>2.47</td><td>5.56</td><td><b>87.22</b></td><td><b>2.14</b></td><td><b>3.61</b></td></tr><tr><td>70%</td><td>84.77</td><td>2.57</td><td>4.84</td><td>83.50</td><td>3.55</td><td>8.00</td><td>85.20</td><td>2.90</td><td>6.30</td><td>84.38</td><td>3.20</td><td>6.45</td><td>87.69</td><td><b>2.03</b></td><td>3.84</td><td><b>87.81</b></td><td>2.12</td><td><b>3.59</b></td></tr><tr><td>90%</td><td>86.22</td><td>2.33</td><td>4.23</td><td>86.16</td><td>2.34</td><td>4.37</td><td>86.75</td><td>2.50</td><td>4.80</td><td>86.40</td><td>2.55</td><td>4.75</td><td>87.89</td><td><b>2.02</b></td><td>3.63</td><td><b>88.64</b></td><td>2.04</td><td><b>3.52</b></td></tr><tr><td>100%</td><td>86.55</td><td>2.18</td><td>4.03</td><td>87.26</td><td>2.54</td><td>4.13</td><td>87.40</td><td>2.35</td><td>4.20</td><td>87.35</td><td>2.40</td><td>4.25</td><td>88.26</td><td>2.01</td><td><b>3.19</b></td><td><b>89.05</b></td><td><b>1.97</b></td><td>3.47</td></tr><tr><td>10% +Gauss</td><td>75.21</td><td>4.01</td><td>11.46</td><td>53.34</td><td>9.75</td><td>21.69</td><td>72.12</td><td>5.61</td><td>13.50</td><td>67.88</td><td>7.69</td><td>17.34</td><td>77.12</td><td>3.95</td><td><b>7.60</b></td><td><b>80.60</b></td><td><b>3.64</b></td><td>8.01</td></tr><tr><td>10% +S_P</td><td>75.14</td><td>3.93</td><td>10.35</td><td>52.73</td><td>9.63</td><td>21.35</td><td>70.65</td><td>5.83</td><td>14.11</td><td>66.40</td><td>7.57</td><td>18.13</td><td>77.15</td><td>3.88</td><td>7.42</td><td><b>81.33</b></td><td><b>3.13</b></td><td><b>6.75</b></td></tr><tr><td>100% +Gauss</td><td>84.24</td><td>2.55</td><td>4.54</td><td>84.80</td><td>3.28</td><td>4.98</td><td>83.01</td><td>2.90</td><td>5.21</td><td>82.14</td><td>3.06</td><td>5.52</td><td>84.20</td><td>2.77</td><td>4.28</td><td><b>85.80</b></td><td><b>2.37</b></td><td><b>4.03</b></td></tr><tr><td>100% +S_P</td><td>84.19</td><td>2.54</td><td>4.23</td><td>82.71</td><td>2.87</td><td>5.12</td><td>82.44</td><td>3.17</td><td>5.59</td><td>81.45</td><td>3.37</td><td>6.08</td><td>84.02</td><td>2.67</td><td>4.33</td><td><b>85.88</b></td><td><b>2.35</b></td><td><b>3.68</b></td></tr></tbody></table>

<table><caption>TABLE II
SEGMENTATION PERFORMANCE COMPARISON UNDER DIFFERENT ANNOTATION RATIOS ON THE MULTI-CENTER PROSTATE MRI DATASET. “+GAUSSIAN” AND “+SALT_PEPPER” INDICATE NOISY INPUT EVALUATION.</caption><thead><tr><th rowspan="2">Ratio</th><th colspan="3">CPS [28]</th><th colspan="3">CoraNet [29]</th><th colspan="3">PLGCL [30]</th><th colspan="3">M3L [31]</th><th colspan="3">SMSUT [2]</th><th colspan="3">SR-MedTS (Ours)</th></tr><tr><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th></tr></thead><tbody><tr><td>10%</td><td>80.67</td><td>3.45</td><td>8.47</td><td>75.58</td><td>4.84</td><td>13.02</td><td>81.55</td><td>3.37</td><td>8.20</td><td>77.17</td><td>4.62</td><td>11.50</td><td>82.95</td><td>3.25</td><td>8.05</td><td><b>83.88</b></td><td><b>3.15</b></td><td><b>6.25</b></td></tr><tr><td>30%</td><td>84.94</td><td>2.95</td><td>6.80</td><td>83.45</td><td>2.82</td><td>6.55</td><td>83.81</td><td>3.14</td><td>7.56</td><td>82.48</td><td>3.80</td><td>8.25</td><td>86.25</td><td><b>2.80</b></td><td><b>6.12</b></td><td><b>86.35</b></td><td>2.82</td><td>6.15</td></tr><tr><td>50%</td><td>86.63</td><td>2.65</td><td>5.39</td><td>85.78</td><td>2.46</td><td>5.27</td><td>85.89</td><td>3.00</td><td>6.61</td><td>84.77</td><td>3.28</td><td>7.20</td><td>87.91</td><td>2.25</td><td>4.75</td><td><b>88.20</b></td><td><b>2.23</b></td><td><b>4.22</b></td></tr><tr><td>70%</td><td>87.22</td><td>2.42</td><td>4.85</td><td>86.83</td><td>2.28</td><td>4.89</td><td>87.10</td><td>2.70</td><td>5.00</td><td>86.30</td><td>2.95</td><td>6.44</td><td>88.60</td><td><b>2.08</b></td><td>4.12</td><td><b>88.78</b></td><td>2.19</td><td><b>4.09</b></td></tr><tr><td>90%</td><td>87.42</td><td>2.38</td><td>4.64</td><td>87.27</td><td>2.20</td><td>4.22</td><td>87.52</td><td>2.55</td><td>4.82</td><td>86.89</td><td>2.76</td><td>5.98</td><td>88.52</td><td><b>2.10</b></td><td>4.17</td><td><b>89.40</b></td><td>2.15</td><td><b>3.03</b></td></tr><tr><td>100%</td><td>87.55</td><td>2.35</td><td>4.39</td><td>87.97</td><td>2.11</td><td>3.85</td><td>88.14</td><td>2.45</td><td>4.54</td><td>87.40</td><td>2.63</td><td>4.90</td><td>89.00</td><td>1.95</td><td>3.09</td><td><b>90.15</b></td><td><b>1.88</b></td><td><b>2.95</b></td></tr><tr><td>10% +Gauss</td><td>78.08</td><td>3.97</td><td>10.76</td><td>73.74</td><td>5.27</td><td>13.88</td><td>79.33</td><td>4.26</td><td>11.41</td><td>75.07</td><td>5.05</td><td>12.98</td><td>80.55</td><td>3.94</td><td>9.27</td><td><b>82.65</b></td><td><b>3.64</b></td><td><b>8.01</b></td></tr><tr><td>10% +S_P</td><td>77.90</td><td>3.98</td><td>10.11</td><td>72.41</td><td>5.47</td><td>13.63</td><td>77.73</td><td>4.67</td><td>12.17</td><td>73.52</td><td>5.43</td><td>13.60</td><td>80.85</td><td><b>3.74</b></td><td>8.65</td><td><b>81.76</b></td><td>3.89</td><td><b>8.11</b></td></tr><tr><td>100% +Gauss</td><td>86.89</td><td>2.99</td><td>5.79</td><td>86.31</td><td>3.01</td><td>5.87</td><td>87.51</td><td>2.96</td><td>5.17</td><td>86.81</td><td>3.16</td><td>6.12</td><td>88.45</td><td>2.55</td><td>4.54</td><td><b>89.80</b></td><td><b>2.37</b></td><td><b>4.03</b></td></tr><tr><td>100% +S_P</td><td>86.20</td><td>3.11</td><td>5.93</td><td>86.54</td><td>3.07</td><td>5.26</td><td>87.39</td><td>3.08</td><td>5.13</td><td>86.63</td><td>3.39</td><td>6.57</td><td>88.20</td><td>2.52</td><td>4.56</td><td><b>89.88</b></td><td><b>2.35</b></td><td><b>3.68</b></td></tr></tbody></table>

<table><caption>TABLE III
ABLATION STUDY ON THE ABDOMINAL DATASET WITH 10% LABELED DATA. PERFORMANCE IS COMPARED ACROSS DIFFERENT MODALITIES (CT, T1IN, T1OUT, T2) AND THE AVERAGE.</caption><thead><tr><th rowspan="2">Models</th><th colspan="3">CT</th><th colspan="3">T1in</th><th colspan="3">T1out</th><th colspan="3">T2</th><th colspan="3">Average</th></tr><tr><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th><th>Dice↑</th><th>ASSD↓</th><th>HD↓</th></tr></thead><tbody><tr><td>SMSUT [2]</td><td>82.25</td><td><b>2.64</b></td><td><b>6.30</b></td><td>81.27</td><td>3.27</td><td>6.58</td><td>82.86</td><td>3.29</td><td>6.31</td><td>86.26</td><td>2.99</td><td>6.08</td><td>83.16</td><td>2.98</td><td>6.31</td></tr><tr><td>w/o L<sub>lpips</sub></td><td>82.31</td><td>2.86</td><td>6.97</td><td>81.49</td><td>3.33</td><td>6.73</td><td>83.80</td><td>3.15</td><td>6.60</td><td>86.45</td><td>3.11</td><td>6.25</td><td>83.51</td><td>3.11</td><td>6.64</td></tr><tr><td>w/o L<sub>DLSR</sub></td><td>82.10</td><td>3.13</td><td>7.40</td><td>80.92</td><td>3.44</td><td>7.05</td><td>83.95</td><td>3.24</td><td>6.85</td><td>85.80</td><td>3.29</td><td>6.65</td><td>83.19</td><td>3.28</td><td>7.04</td></tr><tr><td>w/o DA module</td><td><b>82.50</b></td><td>2.92</td><td>7.12</td><td>81.00</td><td>3.34</td><td>6.85</td><td>84.08</td><td>2.98</td><td>6.46</td><td>86.25</td><td>2.93</td><td>6.17</td><td>83.46</td><td>3.04</td><td>6.65</td></tr><tr><td>Ours</td><td>82.47</td><td>2.86</td><td>7.97</td><td><b>81.88</b></td><td><b>3.03</b></td><td><b>5.70</b></td><td><b>84.94</b></td><td><b>2.58</b></td><td><b>5.35</b></td><td><b>86.52</b></td><td><b>2.42</b></td><td><b>5.72</b></td><td><b>83.95</b></td><td><b>2.72</b></td><td><b>6.18</b></td></tr></tbody></table>

flipping, whereas SR-MedTS successfully identifies the region. This demonstrates the effectiveness of the proposed semantic robustness loss in preserving structural integrity and mitigating pseudo-label drift during translation.

To further assess robustness under noisy conditions, we add Gaussian noise (variance 0.1) and salt-and-pepper noise (density 0.01) to both datasets and evaluate all methods under 10% and 100% annotation ratios (Tables I and II). SR-MedTS consistently maintains superior performance, showing higher stability on boundary metrics such as ASSD and HD. These results confirm that the proposed semantic robustness loss enhances structural preservation and generalization under complex input conditions.

In summary, SR-MedTS outperforms existing methods across standard metrics and exhibits strong robustness and generalization in scenarios with limited supervision and multi-

modal collaboration, highlighting its effectiveness and practical value in medical image segmentation.

### E. Ablation Study

We conduct ablation studies under a 10% annotation ratio on the abdominal dataset to evaluate three key components: the semantic robustness loss, feature-level perceptual loss, and dual-attention module. The base model is SMSUT [2], with components added incrementally.

As shown in Table III, removing the semantic robustness loss ($L_{DLSR}$) increases HD, indicating larger boundary deviations and structural discontinuity, while its guidance during modality translation indirectly improves pseudo-label quality. Removing the perceptual loss ($L_{lpips}$) lowers Dice and increases HD, particularly for fine structures, demonstrating its role in preserving anatomical detail in feature space. Without the dual-attention module, intermediate features are
insufficiently filtered, leading to blurry boundaries and reduced
segmentation accuracy.

Overall, all three components are critical for SR-MedTS,
enhancing performance, robustness, and stability under low-
annotation and complex multi-modal conditions.

V. CONCLUSION

We proposed SR-MedTS, a semi-supervised framework for multi-modal medical image translation and segmentation. By jointly modeling translation and segmentation and incorporating semantic robustness loss, feature-level perceptual loss, and a dual-attention mechanism, SR-MedTS significantly improves segmentation under limited annotations. Experiments on multi-modal abdominal and multi-center prostate MRI datasets demonstrate superior performance, maintaining high structural fidelity and semantic consistency even at 10% annotation.

Despite the dual-stream design, computational complexity
remains manageable via lightweight convolutional blocks and
shared feature extractors. Future work will explore model
compression and efficient attention mechanisms to further
streamline SR-MedTS for clinical deployment.

ACKNOWLEDGMENT

We acknowledge financial support from the National Natu-
ral Science Foundation of China (U23A20481) and the Tianchi
Elite Youth Doctoral Program (CZ002701, CZ002707).

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