Xu Lei, Hu Junjie, Song Ying, Bai Sen, Yi Zhang
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, P R China.
Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, 610065, P R China.
Med Phys. 2021 Apr;48(4):1720-1730. doi: 10.1002/mp.14733. Epub 2021 Mar 12.
Precise segmentation of clinical target volume (CTV) is the key to stomach cancer radiotherapy. We proposed a novel stochastic width-deep neural network (SW-DNN) for better automatically contouring stomach CTV.
Stochastic width-deep neural network was an end-to-end approach, of which the core component was a novel SW mechanism that employed shortcut connections between the encoder and decoder in a random manner, and thus the width of the SW-DNN was stochastically adjustable to obtain improved segmentation results. In total, 150 stomach cancer patient computed tomography (CT) cases with the corresponding CTV labels were collected and used to train and evaluate the SW-DNN. Three common quantitative measures: true positive volume fraction (TPVF), positive predictive value (PPV), and Dice similarity coefficient (DSC) were used to evaluate the segmentation accuracy.
Clinical target volumes calculated by SW-DNN had significant quantitative advantages over three state-of-the-art methods. The average DSC value of SW-DNN was 2.1%, 2.8%, and 3.6% higher than that of three state-of-the-art methods. The average DSC, TPVF, and PPV values of SW-DNN were 2.1%, 4.0%, and 0.3% higher than that of the corresponding constant width DNN.
Stochastic width-deep neural network provided better performance for contouring stomach cancer CTV accurately and efficiently. It is a promising solution in clinical radiotherapy planning for stomach cancer.
精确分割临床靶区（CTV）是胃癌放射治疗的关键。我们提出了一种新型随机宽度深度神经网络（SW-DNN），以更好地自动勾勒胃癌CTV轮廓。
随机宽度深度神经网络是一种端到端方法，其核心组件是一种新型SW机制，该机制以随机方式在编码器和解码器之间采用捷径连接，因此SW-DNN的宽度可随机调整以获得改进的分割结果。总共收集了150例带有相应CTV标签的胃癌患者计算机断层扫描（CT）病例，用于训练和评估SW-DNN。使用三种常见的定量指标：真阳性体积分数（TPVF）、阳性预测值（PPV）和骰子相似系数（DSC）来评估分割准确性。
SW-DNN计算的临床靶区在定量方面比三种最先进的方法具有显著优势。SW-DNN的平均DSC值比三种最先进的方法分别高2.1%、2.8%和3.6%。SW-DNN的平均DSC、TPVF和PPV值比相应的固定宽度DNN分别高2.1%、4.0%和0.3%。
随机宽度深度神经网络为准确、高效地勾勒胃癌CTV轮廓提供了更好的性能。它是胃癌临床放射治疗计划中有前景的解决方案。
