PDF(Pubmed)
Abstract：
Radiotherapy for thoracic and breast tumours is associated with a range of cardiotoxicities. Emerging evidence suggests cardiac substructure doses may be more predictive of specific outcomes, however, quantitative data necessary to develop clinical planning constraints is lacking. Retrospective analysis of patient data is required, which relies on accurate segmentation of cardiac substructures. In this study, a novel model was designed to deliver reliable, accurate, and anatomically consistent segmentation of 18 cardiac substructures on computed tomography (CT) scans. Thirty manually contoured CT scans were included. The proposed multi-stage method leverages deep learning (DL), multi-atlas mapping, and geometric modelling to automatically segment the whole heart, cardiac chambers, great vessels, heart valves, coronary arteries, and conduction nodes. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD), and volume ratio. Performance was reliable, with no errors observed and acceptable variation in accuracy between cases, including in challenging cases with imaging artefacts and atypical patient anatomy. The median DSC range was 0.81-0.93 for whole heart and cardiac chambers, 0.43-0.76 for great vessels and conduction nodes, and 0.22-0.53 for heart valves. For all structures the median MDA was below 6 mm, median HD ranged 7.7-19.7 mm, and median volume ratio was close to one (0.95-1.49) for all structures except the left main coronary artery (2.07). The fully automatic algorithm takes between 9 and 23 min per case. The proposed fully-automatic method accurately delineates cardiac substructures on radiotherapy planning CT scans. Robust and anatomically consistent segmentations, particularly for smaller structures, represents a major advantage of the proposed segmentation approach. The open-source software will facilitate more precise evaluation of cardiac doses and risks from available clinical datasets.
摘要：
胸部和乳腺肿瘤的放射治疗与一系列心脏毒性有关。新出现的证据表明心脏子结构剂量可能更能预测特定结果，然而,缺乏制定临床计划约束所需的定量数据.需要对患者数据进行回顾性分析，这依赖于心脏子结构的精确分割。在这项研究中,一个新的模型被设计来提供可靠的，准确,以及在计算机断层扫描（CT）扫描中对18个心脏亚结构的解剖学一致分割。包括30次手动轮廓CT扫描。提出的多阶段方法利用深度学习(DL)，多图集映射，和几何建模来自动分割整个心脏，心腔,伟大的船只，心脏瓣膜,冠状动脉,和传导节点。使用骰子相似系数(DSC)评估分割性能，平均协议距离(MDA)，Hausdorff距离(HD),和体积比。性能可靠，没有观察到的误差和可接受的精度变化的情况下，包括具有影像伪影和非典型患者解剖结构的挑战性病例。整个心脏和心腔的中位DSC范围为0.81-0.93，大血管和传导节点为0.43-0.76，心脏瓣膜为0.22-0.53。对于所有结构，MDA中位数低于6毫米，中位数HD范围为7.7-19.7毫米，除左主冠状动脉(2.07)外,所有结构的中位体积比接近1(0.95-1.49).全自动算法每次需要9到23分钟。所提出的全自动方法在放射治疗计划CT扫描上准确描绘心脏子结构。稳健和解剖学上一致的分割，特别是对于较小的结构，代表了所提出的分割方法的一个主要优势。开源软件将有助于从可用的临床数据集中更精确地评估心脏剂量和风险。
