用深度学习自动分割宫颈癌MRI图像，提升放疗规划效率与一致性
Two Stage Segmentation of Cervical Tumors using PocketNet
- 采用分两阶段的PocketNet模型，基于T2加权MRI自动分割宫颈及肿瘤
- 肿瘤分割平均DSC超70%，器官分割达80%，在公开数据集上肿瘤DSC为67.3%
- 对不同成像协议具有鲁棒性，适合临床放疗规划中的自动化轮廓勾画
宫颈癌是全球女性第四大常见恶性肿瘤。局部晚期宫颈癌的主要根治性治疗方案为同步放化疗（CRT），包括外照射后接腔内放疗。放疗计划制定的关键步骤是勾画宫颈靶区、相关妇科解剖结构及周围危及器官（OARs）。但人工勾画耗时耗力，且存在可观测者间差异，影响治疗效果。尽管已有多种工具可基于CT图像自动分割OARs和高危临床靶区（HR-CTV），但基于常规T2加权（T2w）磁共振成像（MRI）的深度学习肿瘤分割工具仍属空白。本研究应用新型深度学习模型PocketNet，在T2w MRI上实现宫颈、阴道、子宫及肿瘤的分割。通过五折交叉验证评估模型性能，肿瘤分割平均Dice-Sorensen系数（DSC）超过70%，器官分割超过80%。在癌症影像档案馆（TCIA）公开数据集上的验证显示，模型在肿瘤分割上达到67.3%的DSC，器官分割为80.8%。结果表明PocketNet对对比度协议变化具有鲁棒性，可稳定提供感兴趣区域的可靠分割。
原文摘要 · Abstract (English)
Cervical cancer remains the fourth most common malignancy amongst women worldwide.1 Concurrent chemoradiotherapy (CRT) serves as the mainstay definitive treatment regimen for locally advanced cervical cancers and includes external beam radiation followed by brachytherapy.2 Integral to radiotherapy treatment planning is the routine contouring of both the target tumor at the level of the cervix, associated gynecologic anatomy and the adjacent organs at risk (OARs). However, manual contouring of these structures is both time and labor intensive and associated with known interobserver variability that can impact treatment outcomes. While multiple tools have been developed to automatically segment OARs and the high-risk clinical tumor volume (HR-CTV) using computed tomography (CT) images,3,4,5,6 the development of deep learning-based tumor segmentation tools using routine T2-weighted (T2w) magnetic resonance imaging (MRI) addresses an unmet clinical need to improve the routine contouring of both anatomical structures and cervical cancers, thereby increasing quality and consistency of radiotherapy planning. This work applied a novel deep-learning model (PocketNet) to segment the cervix, vagina, uterus, and tumor(s) on T2w MRI. The performance of the PocketNet architecture was evaluated, when trained on data via five-fold cross validation. PocketNet achieved a mean Dice-Sorensen similarity coefficient (DSC) exceeding 70% for tumor segmentation and 80% for organ segmentation. Validation on a publicly available dataset from The Cancer Imaging Archive (TCIA) demonstrated the models robustness, achieving DSC scores of 67.3% for tumor segmentation and 80.8% for organ segmentation. These results suggest that PocketNet is robust to variations in contrast protocols, providing reliable segmentation of the regions of interest.
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