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Abstract：
Manual delineation of clinical target volumes (CTVs) and organs at risk (OARs) is time-consuming, and automatic contouring tools lack clinical validation. We aimed to construct and validate the use of convolutional neural networks (CNNs) to set better contouring standards for rectal cancer radiotherapy.
We retrospectively collected and evaluated computed tomography (CT) scans of 199 rectal cancer patients treated at our hospital from February 2018 to April 2019. Two CNNs-DeepLabv3+ for extracting high-level semantic information and ResUNet for extracting low-level visual features-were used for the CTV and small intestine contouring, and bladder and femoral head contouring, respectively. Contouring quality was compared using the paired t test. Five-point objective grading was performed independently by two experienced radiation oncologists and verified by a third. The CNN manual correction time was recorded.
CTVs calculated using DeepLabv3+ (CTVDeepLabv3+) had significant quantitative parameter advantages over CTVResUNet (volumetric Dice coefficient, 0.88 vs 0.87, P = 0.0005; surface Dice coefficient, 0.79 vs 0.78, P = 0.008). Among 315 graded cases, DeepLabv3+ obtained the highest scores with 284 cases, consistent with the objective criteria, whereas CTVResUNet had the minimum mean manual correction time (7.29 min). DeepLabv3+ performed better than ResUNet for small intestine contouring and ResUNet performed better for bladder and femoral head contouring. The manual correction time for OARs was <4 min for both models.
CNNs at various feature resolution levels well delineate rectal cancer CTVs and OARs, displaying high quality and requiring shorter computation and manual correction time.
We retrospectively collected and evaluated computed tomography (CT) scans of 199 rectal cancer patients treated at our hospital from February 2018 to April 2019. Two CNNs-DeepLabv3+ for extracting high-level semantic information and ResUNet for extracting low-level visual features-were used for the CTV and small intestine contouring, and bladder and femoral head contouring, respectively. Contouring quality was compared using the paired t test. Five-point objective grading was performed independently by two experienced radiation oncologists and verified by a third. The CNN manual correction time was recorded.
CTVs calculated using DeepLabv3+ (CTVDeepLabv3+) had significant quantitative parameter advantages over CTVResUNet (volumetric Dice coefficient, 0.88 vs 0.87, P = 0.0005; surface Dice coefficient, 0.79 vs 0.78, P = 0.008). Among 315 graded cases, DeepLabv3+ obtained the highest scores with 284 cases, consistent with the objective criteria, whereas CTVResUNet had the minimum mean manual correction time (7.29 min). DeepLabv3+ performed better than ResUNet for small intestine contouring and ResUNet performed better for bladder and femoral head contouring. The manual correction time for OARs was <4 min for both models.
CNNs at various feature resolution levels well delineate rectal cancer CTVs and OARs, displaying high quality and requiring shorter computation and manual correction time.
摘要：
临床目标体积（CTV）和危险器官（OAR）的手动描绘是耗时的，自动轮廓工具缺乏临床验证。我们旨在构建和验证卷积神经网络（CNN）的使用，为直肠癌放疗设置更好的轮廓标准。
我们回顾性收集并评估了2018年2月至2019年4月在我院接受治疗的199例直肠癌患者的计算机断层扫描（CT）扫描。两个CNN-DeepLabv3+用于提取高级语义信息和ResUNet用于提取低级视觉特征-用于CTV和小肠轮廓，膀胱和股骨头轮廓，分别。使用配对t检验比较轮廓质量。五点客观分级由两名经验丰富的放射肿瘤学家独立进行，并由三分之一进行验证。记录CNN手动校正时间。
使用DeepLabv3+（CTVDeepLabv3+）计算的CTV比CTVResUNet具有显著的定量参数优势（体积骰子系数，0.88vs0.87，P=0.0005；表面骰子系数，0.79vs0.78，P=0.008）。在315个分级病例中，DeepLabv3+以284例获得最高分，符合客观标准，而CTVResUNet的平均人工校正时间最短(7.29min).DeepLabv3+在小肠轮廓方面比ResUNet表现更好，ResUNet在膀胱和股骨头轮廓方面表现更好。两种模型的OAR的手动校正时间均<4分钟。
各种特征分辨率水平的CNN很好地描绘了直肠癌CTV和OAR，显示高质量，需要更短的计算和手动校正时间。
我们回顾性收集并评估了2018年2月至2019年4月在我院接受治疗的199例直肠癌患者的计算机断层扫描（CT）扫描。两个CNN-DeepLabv3+用于提取高级语义信息和ResUNet用于提取低级视觉特征-用于CTV和小肠轮廓，膀胱和股骨头轮廓，分别。使用配对t检验比较轮廓质量。五点客观分级由两名经验丰富的放射肿瘤学家独立进行，并由三分之一进行验证。记录CNN手动校正时间。
使用DeepLabv3+（CTVDeepLabv3+）计算的CTV比CTVResUNet具有显著的定量参数优势（体积骰子系数，0.88vs0.87，P=0.0005；表面骰子系数，0.79vs0.78，P=0.008）。在315个分级病例中，DeepLabv3+以284例获得最高分，符合客观标准，而CTVResUNet的平均人工校正时间最短(7.29min).DeepLabv3+在小肠轮廓方面比ResUNet表现更好，ResUNet在膀胱和股骨头轮廓方面表现更好。两种模型的OAR的手动校正时间均<4分钟。
各种特征分辨率水平的CNN很好地描绘了直肠癌CTV和OAR，显示高质量，需要更短的计算和手动校正时间。
