交互式模型提升头颈癌肿瘤分割精度，用户仅需5次点击即可显著优化结果。
Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer
- 采用两阶段点击精修框架，结合深度学习与用户交互
- 无交互时Dice达0.713，5次点击后提升至0.824
- 适合放射治疗规划中需要快速精准勾画的临床医生
头颈癌主要治疗方式为放疗，准确分割原发性大体肿瘤体积（GTVp）至关重要。然而，手动勾画耗时且存在显著观察者差异，完全自动化方法也可能失败。交互式深度学习模型可在保证高性能分割的同时，允许用户在必要时进行修正。本研究探索了头颈癌中GTVp的交互式深度学习分割方法，实现并提出一种新型两阶段交互点击精修（2S-ICR）框架。基于2021年头颈肿瘤（HECKTOR）数据集开发，并在德克萨斯大学安德森癌症中心外部数据集上评估，该框架在无用户交互时取得0.713±0.152的Dice相似系数，经五次交互后提升至0.824±0.099，优于现有方法。
原文摘要 · Abstract (English)
The main treatment modality for oropharyngeal cancer (OPC) is radiotherapy, where accurate segmentation of the primary gross tumor volume (GTVp) is essential. However, accurate GTVp segmentation is challenging due to significant interobserver variability and the time-consuming nature of manual annotation, while fully automated methods can occasionally fail. An interactive deep learning (DL) model offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we examine interactive DL for GTVp segmentation in OPC. We implement state-of-the-art algorithms and propose a novel two-stage Interactive Click Refinement (2S-ICR) framework. Using the 2021 HEad and neCK TumOR (HECKTOR) dataset for development and an external dataset from The University of Texas MD Anderson Cancer Center for evaluation, the 2S-ICR framework achieves a Dice similarity coefficient of 0.713 $\pm$ 0.152 without user interaction and 0.824 $\pm$ 0.099 after five interactions, outperforming existing methods in both cases.
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