ArticleIEEE transactions on medical imaging2021
Disentangle, Align and Fuse for Multimodal and Semi-Supervised Image Segmentation.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- STD-Net: a spatio-temporal decoupling network for multiphasic liver lesion segmentation and characterization.NPJ digital medicine · 2025Article
- CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification.Medical image analysis · 2025Article
- Multimodal deep learning approaches for precision oncology: a comprehensive review.Briefings in bioinformatics · 2024Review
- Semisupervised 3D segmentation of pancreatic tumors in positron emission tomography/computed tomography images using a mutual information minimization and cross-fusion strategy.Quantitative imaging in medicine and surgery · 2024Article
- AnyStar: Domain randomized universal star-convex 3D instance segmentation.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2024Article
- Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series.The journal of machine learning for biomedical imaging · 2023Article
- [A meta-learning based method for segmentation of few-shot magnetic resonance images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- NVTrans-UNet: Neighborhood vision transformer based U-Net for multi-modal cardiac MR image segmentation.Journal of applied clinical medical physics · 2023Article
- Applications of AI in multi-modal imaging for cardiovascular disease.Frontiers in radiology · 2023Review
- Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variations.Frontiers in cardiovascular medicine · 2023Article
- Unsupervised Image Registration towards Enhancing Performance and Explainability in Cardiac and Brain Image Analysis.Sensors (Basel, Switzerland) · 2022Article
- Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images.IEEE transactions on medical imaging · 2021Article
- Representation Disentanglement for Multi-modal Brain MRI Analysis.Information processing in medical imaging : proceedings of the ... conference · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here termed modality, in isolation. Taking advantage of the common information shared between modalities (an organ's anatomy) is beneficial for multi-modality processing and learning. However, we must overcome inherent anatomical misregistrations and disparities in signal intensity across the modalities to obtain this benefit. We present a method that offers improved segmentation accuracy of the modality of interest (over a single input model), by learning to leverage information present in other modalities, even if few (semi-supervised) or no (unsupervised) annotations are available for this specific modality. Core to our method is learning a disentangled decomposition into anatomical and imaging factors. Shared anatomical factors from the different inputs are jointly processed and fused to extract more accurate segmentation masks. Image misregistrations are corrected with a Spatial Transformer Network, which non-linearly aligns the anatomical factors. The imaging factor captures signal intensity characteristics across different modality data and is used for image reconstruction, enabling semi-supervised learning. Temporal and slice pairing between inputs are learned dynamically. We demonstrate applications in Late Gadolinium Enhanced (LGE) and Blood Oxygenation Level Dependent (BOLD) cardiac segmentation, as well as in T2 abdominal segmentation. Code is available at https://github.com/vios-s/multimodal_segmentation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.