Almeida Gonçalo, Figueira Ana Rita, Lencart Joana, Tavares João Manuel R S
Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.
Serviço de Radioterapia, Centro Hospitalar Universitário de São João, Porto, Portugal.
Comput Biol Med. 2022 Jan;140:105107. doi: 10.1016/j.compbiomed.2021.105107. Epub 2021 Dec 2.
Computed Tomography (CT) imaging is used in Radiation Therapy planning, where the treatment is carefully tailored to each patient in order to maximize radiation dose to the target while decreasing adverse effects to nearby healthy tissues. A crucial step in this process is manual organ contouring, which if performed automatically could considerably decrease the time to starting treatment and improve outcomes. Computerized segmentation of male pelvic organs has been studied for decades and deep learning models have brought considerable advances to the field, but improvements are still demanded. A two-step framework for automatic segmentation of the prostate, bladder and rectum is presented: a convolutional neural network enhanced with attention gates performs an initial segmentation, followed by a region-based active contour model to fine-tune the segmentations to each patient's specific anatomy. The framework was evaluated on a large collection of planning CTs of patients who had Radiation Therapy for prostate cancer. The Surface Dice Coefficient improved from 79.41 to 81.00% on segmentation of the prostate, 94.03-95.36% on the bladder and 82.17-83.68% on the rectum, comparing the proposed framework with the baseline convolutional neural network. This study shows that traditional image segmentation algorithms can help improve the immense gains that deep learning models have brought to the medical imaging segmentation field.
计算机断层扫描（CT）成像用于放射治疗计划，在该过程中，治疗方案会根据每位患者的具体情况进行精心定制，以在将辐射剂量最大化至靶区的同时，减少对附近健康组织的不良影响。此过程中的一个关键步骤是手动勾勒器官轮廓，如果能自动完成这一步骤，则可大幅缩短开始治疗的时间并改善治疗效果。对男性盆腔器官的计算机化分割已研究了数十年，深度学习模型为该领域带来了显著进展，但仍有改进的需求。本文提出了一种用于自动分割前列腺、膀胱和直肠的两步框架：一个通过注意力门增强的卷积神经网络进行初始分割，随后是基于区域的主动轮廓模型，以根据每位患者的特定解剖结构对分割结果进行微调。该框架在大量接受前列腺癌放射治疗患者的计划CT图像集上进行了评估。与基线卷积神经网络相比，所提出的框架在前列腺分割上的表面骰子系数从79.41%提高到了81.00%，在膀胱分割上从94.03%提高到了95.36%，在直肠分割上从82.17%提高到了83.68%。这项研究表明，传统图像分割算法有助于提升深度学习模型给医学影像分割领域带来的巨大成果。
