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Updated: Sep 28, 2025
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
Two-stage segmentation network with feature aggregation and multi-level attention mechanism for multi-modality heart
Yuhui Song1, Xiuquan Du2, Yanping Zhang2
1School of Computer Science and Technology, Anhui University, China.
Insights
This study introduces TSFM-Net, a novel two-stage network for segmenting cardiac substructures in multi-modality heart images. The method improves accuracy by addressing target interference and sample imbalance, outperforming existing techniques.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiovascular Disease Research
Background:
- Accurate segmentation of cardiac substructures is crucial for diagnosing and treating cardiovascular diseases.
- Current cardiac image segmentation faces challenges including multi-target interference and imbalanced sample sizes.
- Existing methods struggle to effectively handle the complexity of multi-modality cardiac imaging.
Purpose of the Study:
- To propose a novel two-stage segmentation network, TSFM-Net, to address challenges in cardiac image segmentation.
- To enhance the effectiveness of multi-target feature representation and attention mechanisms.
- To achieve accurate and balanced segmentation of cardiac substructures in multi-modality images.
Main Methods:
- Developed a two-stage segmentation network (TSFM-Net) utilizing an encoder-decoder backbone.
- Incorporated a feature aggregation module (FAM) for multi-level feature representation in Stage 1.
- Designed a multi-level attention mechanism (MLAM) for improved target focus and feature fusion in Stage 2.
Main Results:
- TSFM-Net demonstrated superior segmentation performance on the 2017 MM-WHS multi-modality whole heart image dataset.
- The proposed method achieved better segmentation balance compared to state-of-the-art techniques.
- Feature aggregation and multi-level attention effectively addressed segmentation challenges.
Conclusions:
- TSFM-Net offers a feasible and effective solution for accurate cardiac image segmentation.
- The novel network architecture successfully overcomes limitations of existing segmentation methods.
- The approach shows significant potential for clinical applications in cardiovascular disease management.
Abstract:
Accurate segmentation of cardiac substructures in multi-modality heart images is an important prerequisite for the diagnosis and treatment of cardiovascular diseases. However, the segmentation of cardiac images remains a challenging task due to (1) the interference of multiple targets, (2) the imbalance of sample size. Therefore, in this paper, we propose a novel two-stage segmentation network with feature aggregation and multi-level attention mechanism (TSFM-Net) to comprehensively solve these challenges. Firstly, in order to improve the effectiveness of multi-target features, we adopt the encoder-decoder structure as the backbone segmentation framework and design a feature aggregation module (FAM) to realize the multi-level feature representation (Stage1). Secondly, because the segmentation results obtained from Stage1 are limited to the decoding of single scale feature maps, we design a multi-level attention mechanism (MLAM) to assign more attention to the multiple targets, so as to get multi-level attention maps. We fuse these attention maps and concatenate the output of Stage1 to carry out the second segmentation to get the final segmentation result (Stage2). The proposed method has better segmentation performance and balance on 2017 MM-WHS multi-modality whole heart images than the state-of-the-art methods, which demonstrates the feasibility of TSFM-Net for accurate segmentation of heart images.
