用多序列MRI数据精准分割心肌病变区域，助力个性化治疗
Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNN
- 分两阶段的级联网络融合多种MRI影像，逐步细化分割结果
- 对瘢痕和水肿区域的DSC达62.31%和63.78%，精度超80%
- 适合心血管影像分析、心脏病智能诊疗等场景
心肌梗死（MI）是全球最常见的心血管疾病之一，准确评估心肌组织存活率对诊断与治疗规划至关重要。本研究结合晚期钆增强（LGE）、T2加权（T2）及平衡稳态自由进动（bSSFP） cine MRI三种成像模态，实现左/右心室、健康与瘢痕心肌、水肿区域的语义分割。提出多序列级联精炼卷积网络（MS-CaRe-CNN），第一阶段不考虑组织存活率，生成主要解剖结构预测；第二阶段在此基础上进一步区分健康、瘢痕与水肿区域。方法采用5折集成，对瘢痕区域实现62.31% DSC和82.65%精度，对瘢痕+水肿联合区域达63.78% DSC和87.69%精度。这些对小而复杂结构的优异表现证明了该方法在评估心肌组织存活率方面的有效性，可支持个性化治疗规划等下游任务。
原文摘要 · Abstract (English)
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases and consequently, a major cause for mortality and morbidity worldwide. Accurate assessment of myocardial tissue viability for post-MI patients is critical for diagnosis and treatment planning, e.g. allowing surgical revascularization, or to determine the risk of adverse cardiovascular events in the future. Fine-grained analysis of the myocardium and its surrounding anatomical structures can be performed by combining the information obtained from complementary medical imaging techniques. In this work, we use late gadolinium enhanced (LGE) magnetic resonance (MR), T2-weighted (T2) MR and balanced steady-state free precession (bSSFP) cine MR in order to semantically segment the left and right ventricle, healthy and scarred myocardial tissue, as well as edema. To this end, we propose the Multi-Sequence Cascading Refinement CNN (MS-CaRe-CNN), a 2-stage CNN cascade that receives multi-sequence data and generates predictions of the anatomical structures of interest without considering tissue viability at Stage 1. The prediction of Stage 1 is then further refined in Stage 2, where the model additionally distinguishes myocardial tissue based on viability, i.e. healthy, scarred and edema regions. Our proposed method is set up as a 5-fold ensemble and semantically segments scar tissue achieving 62.31% DSC and 82.65% precision, as well as 63.78% DSC and 87.69% precision for the combined scar and edema region. These promising results for such small and challenging structures confirm that MS-CaRe-CNN is well-suited to generate semantic segmentations to assess the viability of myocardial tissue, enabling downstream tasks like personalized therapy planning.
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