Curvilinear Structure-preserving Unpaired Cross-domain Medical Image Translation

• URL: http://arxiv.org/abs/2510.19679v1
• Date: Wed, 22 Oct 2025 15:24:32 GMT
• Title: Curvilinear Structure-preserving Unpaired Cross-domain Medical Image Translation
• Authors: Zihao Chen, Yi Zhou, Xudong Jiang, Li Chen, Leopold Schmetterer, Bingyao Tan, Jun Cheng,
• Abstract summary: Unpaired image-to-image translation has emerged as a crucial technique in medical imaging.<n>Existing approaches often distort fine curvilinear structures, such as microvasculature.<n>We propose Curvilinear Structure-preserving Translation (CST), a framework that explicitly preserves fine curvilinear structures during unpaired translation.
• Score: 29.978321741632197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unpaired image-to-image translation has emerged as a crucial technique in medical imaging, enabling cross-modality synthesis, domain adaptation, and data augmentation without costly paired datasets. Yet, existing approaches often distort fine curvilinear structures, such as microvasculature, undermining both diagnostic reliability and quantitative analysis. This limitation is consequential in ophthalmic and vascular imaging, where subtle morphological changes carry significant clinical meaning. We propose Curvilinear Structure-preserving Translation (CST), a general framework that explicitly preserves fine curvilinear structures during unpaired translation by integrating structure consistency into the training. Specifically, CST augments baseline models with a curvilinear extraction module for topological supervision. It can be seamlessly incorporated into existing methods. We integrate it into CycleGAN and UNSB as two representative backbones. Comprehensive evaluation across three imaging modalities: optical coherence tomography angiography, color fundus and X-ray coronary angiography demonstrates that CST improves translation fidelity and achieves state-of-the-art performance. By reinforcing geometric integrity in learned mappings, CST establishes a principled pathway toward curvilinear structure-aware cross-domain translation in medical imaging.

Related papers

• Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation [51.509572354327986]
  This work introduces a novel two-stage (structure- and report-learning) framework tailored for Computed Tomography Report Generation (CTRG)<n>In the first stage, a set of learnable structure-specific visual queries observe corresponding structures in a CT image. The resulting observation tokens are contrasted with structure-specific textual features extracted from the accompanying radiology report with a structure-wise image-text contrastive loss.<n>In the second stage, the visual structure queries are frozen and used to select the critical image patch embeddings depicting each anatomical structure, minimizing distractions from irrelevant areas while reducing memory consumption.
  arXiv  Detail & Related papers  (2026-03-05T07:07:07Z)
• TGC-Net: A Structure-Aware and Semantically-Aligned Framework for Text-Guided Medical Image Segmentation [56.09179939570486]
  We propose TGC-Net, a CLIP-based framework focusing on parameter-efficient, task-specific adaptations.<n>TGC-Net achieves state-of-the-art performance with substantially fewer trainable parameters, including notable Dice gains on challenging benchmarks.
  arXiv  Detail & Related papers  (2025-12-24T12:06:26Z)
• Plasticine: A Traceable Diffusion Model for Medical Image Translation [79.39689106440389]
  We propose Plasticine, to the best of our knowledge, the first end-to-end image-to-image translation framework explicitly designed with traceability as a core objective.<n>Our method combines intensity translation and spatial transformation within a denoising diffusion framework.<n>This design enables the generation of synthetic images with interpretable intensity transitions and spatially coherent deformations, supporting pixel-wise traceability throughout the translation process.
  arXiv  Detail & Related papers  (2025-12-20T18:01:57Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• PULSE: A Unified Multi-Task Architecture for Cardiac Segmentation, Diagnosis, and Few-Shot Cross-Modality Clinical Adaptation [0.27998963147546135]
  We introduce PULSE, a multi-task vision-language framework built on self-supervised representations and optimized through a composite supervision strategy.<n>A multi-scale token reconstruction decoder enables anatomical segmentation, while shared global representations support disease classification and clinically grounded text output.<n>Unlike prior task-specific pipelines, PULSE learns task-invariant cardiac priors, generalizes robustly across datasets, and can be adapted to new imaging modalities with minimal supervision.
  arXiv  Detail & Related papers  (2025-12-03T14:49:01Z)
• Self-Supervised Anatomical Consistency Learning for Vision-Grounded Medical Report Generation [61.350584471060756]
  Vision-grounded medical report generation aims to produce clinically accurate descriptions of medical images.<n>We propose Self-Supervised Anatomical Consistency Learning (SS-ACL) to align generated reports with corresponding anatomical regions.<n>SS-ACL constructs a hierarchical anatomical graph inspired by the invariant top-down inclusion structure of human anatomy.
  arXiv  Detail & Related papers  (2025-09-30T08:59:06Z)
• GRASPing Anatomy to Improve Pathology Segmentation [67.98147643529309]
  We introduce GRASP, a modular plug-and-play framework that enhances pathology segmentation models.<n>We evaluate GRASP on two PET/CT datasets, conduct systematic ablation studies, and investigate the framework's inner workings.
  arXiv  Detail & Related papers  (2025-08-05T12:26:36Z)
• RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency [11.813883157319381]
  We propose a novel framework that aligns gene and image features using a ranking-based alignment loss.<n>To further enhance the alignment's stability, we employ self-supervised knowledge distillation with a teacher-student network architecture.
  arXiv  Detail & Related papers  (2024-11-22T17:08:28Z)
• A Multimodal Approach Combining Structural and Cross-domain Textual Guidance for Weakly Supervised OCT Segmentation [12.948027961485536]
  We propose a novel Weakly Supervised Semantic (WSSS) approach that integrates structural guidance with text-driven strategies to generate high-quality pseudo labels. Our method achieves state-of-the-art performance, highlighting its potential to improve diagnostic accuracy and efficiency in medical imaging.
  arXiv  Detail & Related papers  (2024-11-19T16:20:27Z)
• ContourDiff: Unpaired Image-to-Image Translation with Structural Consistency for Medical Imaging [14.487188068402178]
  We introduce a novel metric to quantify the structural bias between domains which must be considered for proper translation. We then propose ContourDiff, a novel image-to-image translation algorithm that leverages domain-invariant anatomical contour representations. We evaluate our method on challenging lumbar spine and hip-and-thigh CT-to-MRI translation tasks.
  arXiv  Detail & Related papers  (2024-03-16T03:33:52Z)
• ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction [59.971808117043366]
  ReShapeIT represents an anatomical structure with an implicit template field shared within the same category. It ensures the implicit template field generates valid templates by strengthening the constraint of the correspondence between the instance shape and the template shape. A template Interaction Module is introduced to reconstruct unseen shapes by interacting the valid template shapes with the instance-wise latent codes.
  arXiv  Detail & Related papers  (2023-12-11T07:09:32Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization [0.43012765978447565]
  Cycle-consistent Generative Adversarial Networks have been used to harmonize image sets between a source and target domain. These methods are prone to instability, contrast inversion, intractable manipulation of pathology, and steganographic mappings which limit their reliable adoption in real-world medical imaging. We propose a segmentation-renormalized image translation framework to reduce inter-scanner harmonization while preserving anatomical layout.
  arXiv  Detail & Related papers  (2021-02-11T23:53:51Z)
• Flow-based Deformation Guidance for Unpaired Multi-Contrast MRI Image-to-Image Translation [7.8333615755210175]
  In this paper, we introduce a novel approach to unpaired image-to-image translation based on the invertible architecture. We utilize the temporal information between consecutive slices to provide more constraints to the optimization for transforming one domain to another in unpaired medical images.
  arXiv  Detail & Related papers  (2020-12-03T09:10:22Z)
• CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging [90.78899127463445]
  We propose a generic and unified convolution neural network for the segmentation of curvilinear structures. We introduce a new curvilinear structure segmentation network (CS2-Net), which includes a self-attention mechanism in the encoder and decoder.
  arXiv  Detail & Related papers  (2020-10-15T03:06:37Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.