Abstract Autosegmentation of image guidance (IG) scans is crucial for streamlining and optimising delivered dose calculation in radiotherapy. By accounting for interfraction motion, daily delivered dose can be accumulated and incorporated into automated systems for adaptive radiotherapy. Autosegmentation of IG scans is challenging due to poorer image quality than typical planning kilovoltage computed tomography (kVCT) systems, and the resulting reduction of soft tissue contrast in regions such as the pelvis makes organ boundaries less distinguishable. Current autosegmentation solutions generally involve propagation of planning contours to the IG scan by deformable image registration (DIR). Here, we present a novel approach for primary autosegmentation of the rectum on megavoltage IG scans acquired during prostate radiotherapy, based on the Chan-Vese algorithm. Pre-processing steps such as Hounsfield unit/intensity scaling, identifying search regions, dealing with air, and handling the prostate, are detailed. Post-processing features include identification of implausible contours (nominally those affected by muscle or air), 3D self-checking, smoothing, and interpolation. In cases where the algorithm struggles, the best estimate on a given slice may revert to the propagated kVCT rectal contour. Algorithm parameters were optimised systematically for a training cohort of 26 scans, and tested on a validation cohort of 30 scans, from 10 patients. Manual intervention was not required. Comparing Chan-Vese autocontours with contours manually segmented by an experienced clinical oncologist achieved a mean Dice Similarity Coefficient of 0.78 (SE < 0.011). This was comparable with DIR methods for kVCT and CBCT published in the literature. The autosegmentation system was developed within the VoxTox Research Programme for accumulation of delivered dose to the rectum in prostate radiotherapy, but may have applicability to further anatomical sites and imaging modalities.

中文翻译：

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兆电压图像引导扫描直肠自动分割

摘要 图像引导 (IG) 扫描的自动分割对于简化和优化放射治疗中的输送剂量计算至关重要。通过考虑分割运动，可以累积每日输送剂量并将其纳入自适应放射治疗的自动化系统中。IG 扫描的自动分割具有挑战性，因为图像质量比典型的计划千伏计算机断层扫描 (kVCT) 系统差，并且导致骨盆等区域的软组织对比度降低，使器官边界难以区分。当前的自动分割解决方案通常涉及通过可变形图像配准 (DIR) 将规划轮廓传播到 IG 扫描。在这里，我们提出了一种基于 Chan-Vese 算法的新方法，用于在前列腺放疗期间获得的兆伏 IG 扫描中对直肠进行初级自动分割。详细介绍了预处理步骤，例如亨斯菲尔德单位/强度缩放、识别搜索区域、处理空气和处理前列腺。后处理功能包括识别不可信的轮廓（通常是受肌肉或空气影响的轮廓）、3D 自检、平滑和插值。在算法遇到困难的情况下，给定切片的最佳估计可能会恢复为传播的 kVCT 直肠轮廓。针对 26 次扫描的训练队列系统地优化了算法参数，并在来自 10 名患者的 30 次扫描的验证队列上进行了测试。不需要人工干预。将 Chan-Vese 自动轮廓与经验丰富的临床肿瘤学家手动分割的轮廓进行比较，平均 Dice 相似系数为 0.78 (SE < 0.011)。这与文献中发表的 kVCT 和 CBCT 的 DIR 方法相当。该自动分割系统是在 VoxTox 研究计划内开发的，用于在前列腺放射治疗中积累直肠的输送剂量，但可能适用于进一步的解剖部位和成像方式。