使用级联细化CNN的多源多序列心肌病理分割
Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNN
AI总结：
本文提出多序列级联细化CNN（MS-CaRe-CNN），融合LGE、T2和bSSFP MRI数据，两阶段分割心室、瘢痕和水肿，5折集成达62.31% DSC，支持个性化治疗规划。
摘要：
心肌梗死（MI）是最普遍的心血管疾病之一，也是全球死亡率和发病率的主要原因。对于心肌梗死后患者，准确评估心肌组织活力对于诊断和治疗规划至关重要，例如允许手术血运重建，或确定未来不良心血管事件的风险。通过结合互补医学成像技术获得的信息，可以对心肌及其周围解剖结构进行细粒度分析。在本工作中，我们使用晚期钆增强（LGE）磁共振（MR）、T2加权（T2）MR和平衡稳态自由进动（bSSFP）电影MR，对左心室和右心室、健康和瘢痕心肌组织以及水肿进行语义分割。为此，我们提出了多序列级联细化CNN（MS-CaRe-CNN），这是一个2阶段CNN级联，接收多序列数据并在第一阶段生成感兴趣解剖结构的预测，而不考虑组织活力。第一阶段的预测在第二阶段进一步细化，其中模型还根据活力区分心肌组织，即健康、瘢痕和水肿区域。我们提出的方法设置为5折集成，对瘢痕组织进行语义分割，达到62.31%的DSC和82.65%的精确度，以及对于瘢痕和水肿合并区域达到63.78%的DSC和87.69%的精确度。这些对于如此小而具有挑战性的结构的有希望的结果证实，MS-CaRe-CNN非常适合生成语义分割以评估心肌组织的活力，从而实现个性化治疗规划等下游任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
