Li Jing, Song Ying, Wu Yongchang, Liang Lan, Li Guangjun, Bai Sen
Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
Machine Intelligence Laboratory, College of Computer Science, Chengdu, China.
Front Oncol. 2023 Sep 5;13:1158315. doi: 10.3389/fonc.2023.1158315. eCollection 2023.
Image segmentation can be time-consuming and lacks consistency between different oncologists, which is essential in conformal radiotherapy techniques. We aimed to evaluate automatic delineation results generated by convolutional neural networks (CNNs) from geometry and dosimetry perspectives and explore the reliability of these segmentation tools in rectal cancer.
Forty-seven rectal cancer cases treated from February 2018 to April 2019 were randomly collected retrospectively in our cancer center. The oncologists delineated regions of interest (ROIs) on planning CT images as the ground truth, including clinical target volume (CTV), bladder, small intestine, and femoral heads. The corresponding automatic segmentation results were generated by DeepLabv3+ and ResUNet, and we also used Atlas-Based Autosegmentation (ABAS) software for comparison. The geometry evaluation was carried out using the volumetric Dice similarity coefficient (DSC) and surface DSC, and critical dose parameters were assessed based on replanning optimized by clinically approved or automatically generated CTVs and organs at risk (OARs), , the Plan and Plan. Pearson test was used to explore the correlation between geometric metrics and dose parameters.
In geometric evaluation, DeepLabv3+ performed better in DCS metrics for the CTV (volumetric DSC, mean = 0.96, P< 0.01; surface DSC, mean = 0.78, P< 0.01) and small intestine (volumetric DSC, mean = 0.91, P< 0.01; surface DSC, mean = 0.62, P< 0.01), ResUNet had advantages in volumetric DSC of the bladder (mean = 0.97, P< 0.05). For critical dose parameters analysis between Plan and Plan, there was a significant difference for target volumes (P< 0.01), and no significant difference was found for the ResUNet-generated small intestine (P > 0.05). For the correlation test, a negative correlation was found between DSC metrics (volumetric, surface DSC) and dosimetric parameters (δD95, δD95, HI, CI) for target volumes (P< 0.05), and no significant correlation was found for most tests of OARs (P > 0.05).
CNNs show remarkable repeatability and time-saving in automatic segmentation, and their accuracy also has a certain potential in clinical practice. Meanwhile, clinical aspects, such as dose distribution, may need to be considered when comparing the performance of auto-segmentation methods.
图像分割可能耗时且不同肿瘤学家之间缺乏一致性，而这在适形放疗技术中至关重要。我们旨在从几何和剂量学角度评估卷积神经网络（CNN）生成的自动轮廓勾画结果，并探讨这些分割工具在直肠癌中的可靠性。
回顾性随机收集了2018年2月至2019年4月在我们癌症中心接受治疗的47例直肠癌病例。肿瘤学家在计划CT图像上勾画感兴趣区域（ROI）作为参考标准，包括临床靶体积（CTV）、膀胱、小肠和股骨头。相应的自动分割结果由DeepLabv3+和ResUNet生成，我们还使用基于图谱的自动分割（ABAS）软件进行比较。使用体积骰子相似系数（DSC）和表面DSC进行几何评估，并基于由临床批准或自动生成的CTV和危及器官（OAR）重新规划优化后的计划评估关键剂量参数。使用Pearson检验探索几何指标与剂量参数之间的相关性。
在几何评估中，DeepLabv3+在CTV的DCS指标（体积DSC，平均值 = 0.96，P < 0.01；表面DSC，平均值 = 0.78，P < 0.01）和小肠（体积DSC，平均值 = 0.91，P < 0.01；表面DSC，平均值 = 0.62，P < 0.01）方面表现更好，ResUNet在膀胱的体积DSC方面具有优势（平均值 = 0.97，P < 0.05）。对于计划和计划之间的关键剂量参数分析，靶体积存在显著差异（P < 0.01），而ResUNet生成的小肠没有显著差异（P > 0.05）。对于相关性测试，发现靶体积的DSC指标（体积、表面DSC）与剂量学参数（δD95、δD95、HI、CI）之间存在负相关（P < 0.05），而对于大多数OAR测试没有显著相关性（P > 0.05）。
CNN在自动分割中显示出显著的可重复性和省时性，其准确性在临床实践中也具有一定潜力。同时，在比较自动分割方法的性能时，可能需要考虑剂量分布等临床因素。
