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.
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。