通过融合影像元数据，实现心脏图像的分步精准分割。
Compositional Segmentation of Cardiac Images Leveraging Metadata
- 分步式架构先定位心脏再分割各区域，提升精度。
- 结合影像采集时的元数据，显著优化分割效果。
- 适用于心脏影像分析，尤其适合多模态数据研究者。
心脏图像分割对自动化心脏功能评估及结构变化监测至关重要。受图像分析中自粗到精策略启发，本文提出一种新型多任务组合分割方法，可同时定位心脏并完成感兴趣区域的部件级分割。实验表明，该组合方法优于直接解剖结构分割。进一步提出跨模态特征融合（CMFI）模块，利用成像过程中收集的元数据信息。在两种模态（MRI与超声）上使用公开数据集M&Ms-2和CAMUS进行实验，验证了所提组合分割方法及嵌入元数据的CMFI模块的有效性。代码已开源：https://github.com/kabbas570/CompSeg-MetaData。
原文摘要 · Abstract (English)
Cardiac image segmentation is essential for automated cardiac function assessment and monitoring of changes in cardiac structures over time. Inspired by coarse-to-fine approaches in image analysis, we propose a novel multitask compositional segmentation approach that can simultaneously localize the heart in a cardiac image and perform part-based segmentation of different regions of interest. We demonstrate that this compositional approach achieves better results than direct segmentation of the anatomies. Further, we propose a novel Cross-Modal Feature Integration (CMFI) module to leverage the metadata related to cardiac imaging collected during image acquisition. We perform experiments on two different modalities, MRI and ultrasound, using public datasets, Multi-disease, Multi-View, and Multi-Centre (M&Ms-2) and Multi-structure Ultrasound Segmentation (CAMUS) data, to showcase the efficiency of the proposed compositional segmentation method and Cross-Modal Feature Integration module incorporating metadata within the proposed compositional segmentation network. The source code is available: https://github.com/kabbas570/CompSeg-MetaData.
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