Crop and Couple: cardiac image segmentation using interlinked specialist networks
Not on the map. Why?
Abstract (arXiv)
Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation. We propose a novel strategy that performs segmentation using specialist networks that focus on a single anatomy (left ventricle, right ventricle, or myocardium). Given an input long-axis cardiac MR image, our method performs a ternary segmentation in the first stage to identify these anatomical regions, followed by cropping the original image to focus subsequent processing on the anatomical regions. The specialist networks are coupled through an attention mechanism that performs cross-attention to interlink features from different anatomies, serving as a soft relative shape prior. Central to our approach is an additive attention block (E-2A block), which is used throughout our architecture thanks to its efficiency.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 14 Feb 2024.
Builds on
11 of 21 references.
References · 11
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
- 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
- 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 261
- 2019Deep learning for cardiac image segmentation: A reviewcited by 131
- 2021UTNet: A Hybrid Transformer Architecture for Medical Image Segmentationcited by 116
- 2023SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applicationscited by 66
- 2021Multi-Compound Transformer for Accurate Biomedical Image Segmentationcited by 42
- 2022TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformerscited by 21
- 2021Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transitioncited by 9
Built on by
Citing papers per year: 2024: 2.
2 papers.