Cross-Modal MRI Ovary Segmentation in Endometriosis Using Unpaired TVUS Prototype Priors
- URL: http://arxiv.org/abs/2608.18515v1
- Date: Wed, 19 Aug 2026 04:09:42 GMT
- Title: Cross-Modal MRI Ovary Segmentation in Endometriosis Using Unpaired TVUS Prototype Priors
- Abstract summary: Transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) provide complementary information for endometriosis image analysis.<n>To tackle the increased difficulty of ovary segmentation in MRI due to ovaries' small target size and ambiguous boundaries with surrounding pelvic structures, we propose a dual branch framework for ovary segmentation across TVUS and MRI.
- Score: 33.630023489749995
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) provide complementary information for endometriosis image analysis, yet existing studies mainly focus on single-modality analysis or disease classification, leaving cross-modal ovarian segmentation largely unexplored. In this work, to tackle the increased difficulty of ovary segmentation in MRI due to ovaries' small target size and ambiguous boundaries with surrounding pelvic structures, we propose a dual branch framework for ovary segmentation across TVUS and MRI. More specifically, by adapting MedSAM3 with TVUS-derived prototype bank, we aim to align anatomically consistent feature representations across both modalities. Extensive experiments are conducted on endometriosis-related TVUS and MRI datasets. We observe quantitative and qualitative improvements of over 5 percentage points for the proposed dual-branch approach compared with multiple state-of-the-art methods. Furthermore, our ablation study shows the contribution of individual components such as the prototype bank and the importance of warm-up pretraining in the source TVUS domain.
Related papers
- Multiple Myeloma Lesion Segmentation on Whole-Body Diffusion-Weighted Imaging via Efficient Anatomical Anticipation and Multimodal Confirmation [28.876740706448345]
We propose a two-stage framework for MM lesion segmentation on WB-DWI.<n>In the first stage, we train a bone ROI generation model from ADC images without dedicated bone labels.<n>In the second stage, we propose Anatomy-guided Multimodal U-Net.
arXiv Detail & Related papers (2026-09-05T16:08:29Z) - A Unified Attention U-Net Framework for Cross-Modality Tumor Segmentation in MRI and CT [0.0]
This study presents a unified Attention U-Net architecture trained jointly on MRI (BraTS 2021) and CT (LIDC-IDRI) datasets.<n>Our proposed pipeline incorporates modality-harmonized preprocessing, attention-gated skip connections, and a modality-aware Focal Tversky loss function.
arXiv Detail & Related papers (2026-01-07T23:50:45Z) - Pancreas Part Segmentation under Federated Learning Paradigm [18.670706296080084]
We present the first federated learning (FL) approach for pancreas part(head, body and tail) segmentation in MRI.<n>This contribution tackles two fundamental challenges: first, the technical complexity of pancreas part delineation in MRI, and second the data scarcity problem that has hindered prior approaches.<n>We introduce a privacy-preserving FL framework that enables collaborative model training across seven medical institutions without direct data sharing.
arXiv Detail & Related papers (2025-09-28T01:42:43Z) - Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation [0.6597195879147557]
We propose a semi-supervised deep learning-based approach to integrate multi-sequence MRI data for the segmentation of carotid artery vessel wall and plaque.<n>Our approach is evaluated on 52 patients with arteriosclerosis, each with five MRI sequences.
arXiv Detail & Related papers (2025-07-10T07:31:31Z) - Enhanced MRI Representation via Cross-series Masking [48.09478307927716]
Cross-Series Masking (CSM) Strategy for effectively learning MRI representation in a self-supervised manner.<n>Method achieves state-of-the-art performance on both public and in-house datasets.
arXiv Detail & Related papers (2024-12-10T10:32:09Z) - Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation [48.107348956719775]
We introduce Mask-Enhanced SAM (M-SAM), an innovative architecture tailored for 3D tumor lesion segmentation.
We propose a novel Mask-Enhanced Adapter (MEA) within M-SAM that enriches the semantic information of medical images with positional data from coarse segmentation masks.
Our M-SAM achieves high segmentation accuracy and also exhibits robust generalization.
arXiv Detail & Related papers (2024-03-09T13:37:02Z) - Reconstructing the somatotopic organization of the corticospinal tract
remains a challenge for modern tractography methods [55.07297021627281]
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body.
Diffusion MRI tractography is the only method that enables the study of the anatomy and variability of the CST pathway in human health.
arXiv Detail & Related papers (2023-06-09T02:05:40Z) - RCA-IUnet: A residual cross-spatial attention guided inception U-Net
model for tumor segmentation in breast ultrasound imaging [0.6091702876917281]
The article introduces an efficient residual cross-spatial attention guided inception U-Net (RCA-IUnet) model with minimal training parameters for tumor segmentation.
The RCA-IUnet model follows U-Net topology with residual inception depth-wise separable convolution and hybrid pooling layers.
Cross-spatial attention filters are added to suppress the irrelevant features and focus on the target structure.
arXiv Detail & Related papers (2021-08-05T10:35:06Z) - Act Like a Radiologist: Towards Reliable Multi-view Correspondence
Reasoning for Mammogram Mass Detection [49.14070210387509]
We propose an Anatomy-aware Graph convolutional Network (AGN) for mammogram mass detection.
AGN is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability.
Experiments on two standard benchmarks reveal that AGN significantly exceeds the state-of-the-art performance.
arXiv Detail & Related papers (2021-05-21T06:48:34Z) - Global Guidance Network for Breast Lesion Segmentation in Ultrasound
Images [84.03487786163781]
We develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection modules.
Our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation.
arXiv Detail & Related papers (2021-04-05T13:15:22Z) - On segmentation of pectoralis muscle in digital mammograms by means of
deep learning [1.7114784273243784]
The present paper introduces a two-step segmentation strategy based on a combined use of data-driven prediction and graph-based image processing.
The proposed method employs a convolutional neural network (CNN) which is designed to predict the location of breast-pectoral boundary.
The results of comparative analysis show considerable improvement over state-of-the-art, while offering the possibility of model-free and fully automatic processing.
arXiv Detail & Related papers (2020-08-29T03:38:11Z)
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.