A Unified Attention U-Net Framework for Cross-Modality Tumor Segmentation in MRI and CT

• URL: http://arxiv.org/abs/2601.06187v1
• Date: Wed, 07 Jan 2026 23:50:45 GMT
• Title: A Unified Attention U-Net Framework for Cross-Modality Tumor Segmentation in MRI and CT
• Authors: Nishan Rai, Pushpa R. Dahal,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study presents a unified Attention U-Net architecture trained jointly on MRI (BraTS 2021) and CT (LIDC-IDRI) datasets to investigate the generalizability of a single model across diverse imaging modalities and anatomical sites. Our proposed pipeline incorporates modality-harmonized preprocessing, attention-gated skip connections, and a modality-aware Focal Tversky loss function. To the best of our knowledge, this study is among the first to evaluate a single Attention U-Net trained simultaneously on separate MRI (BraTS) and CT (LIDC-IDRI) tumor datasets, without relying on modality-specific encoders or domain adaptation. The unified model demonstrates competitive performance in terms of Dice coefficient, IoU, and AUC on both domains, thereby establishing a robust and reproducible baseline for future research in cross-modality tumor segmentation.

Related papers

• RL-U$^2$Net: A Dual-Branch UNet with Reinforcement Learning-Assisted Multimodal Feature Fusion for Accurate 3D Whole-Heart Segmentation [0.624829068285122]
  We propose a dual-branch U-Net architecture enhanced by reinforcement learning for feature alignment.<n>The model employs a dual-branch U-shaped network to process CT and MRI patches in parallel, and introduces a novel RL-XAlign module.<n> Experimental results on the publicly available MM-WHS 2017 dataset demonstrate that the proposed RL-U$2$Net outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-04T16:12:06Z)
• 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)
• An Arbitrary-Modal Fusion Network for Volumetric Cranial Nerves Tract Segmentation [21.228897192093573]
  We propose a novel arbitrary-modal fusion network for volumetric cranial nerves (CNs) tract segmentation, called CNTSeg-v2.<n>Our model encompasses an Arbitrary-Modal Collaboration Module (ACM) designed to effectively extract informative features from other auxiliary modalities.<n>Our CNTSeg-v2 achieves state-of-the-art segmentation performance, outperforming all competing methods.
  arXiv  Detail & Related papers  (2025-05-05T06:00:41Z)
• Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging [0.510750648708198]
  Whole-heart segmentation from CT and MRI scans is crucial for cardiovascular disease analysis.<n>Existing methods struggle with modality-specific biases and the need for extensive labeled datasets.<n>We propose a foundation model for whole-heart segmentation using a self-supervised learning framework based on a student-teacher architecture.
  arXiv  Detail & Related papers  (2025-03-24T14:47:54Z)
• Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion [3.8153469790341084]
  We propose a novel approach that leverages self-supervised learning to synergize multi-modal information across domains. We constructed a large-scale pre-training dataset and pretrained MCSP model by leveraging proposed self-supervised paradigms. Our study contributes a significant advancement in the fusion of fMRI and EEG, marking a novel integration of cross-domain features.
  arXiv  Detail & Related papers  (2024-09-27T20:25:17Z)
• Cross-modality Guidance-aided Multi-modal Learning with Dual Attention for MRI Brain Tumor Grading [47.50733518140625]
  Brain tumor represents one of the most fatal cancers around the world, and is very common in children and the elderly. We propose a novel cross-modality guidance-aided multi-modal learning with dual attention for addressing the task of MRI brain tumor grading.
  arXiv  Detail & Related papers  (2024-01-17T07:54:49Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• 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)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)
• Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation [71.89867233426597]
  In this paper, we aim to exploit the prior knowledge learned from one modality to improve the segmentation performance on another modality. We propose a novel Mutual Knowledge Distillation scheme to thoroughly exploit the modality-shared knowledge. Experimental results on the public multi-class cardiac segmentation data, i.e., MMWHS 2017, show that our method achieves large improvements on CT segmentation.
  arXiv  Detail & Related papers  (2020-10-04T10:25:13Z)

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.