Fully automated organ segmentation in male pelvic CT images
Fully automated organ segmentation in male pelvic CT images
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DOI：
10.1088/1361-6560/aaf11c
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发表时间：
2018-12-01
影响因子：
3.5
通讯作者：
Jiang, Steve
中科院分区：
文献类型：
--
作者：
Balagopal, Anjali;Kazemifar, Samaneh;Jiang, Steve
前列腺及其周围危险器官的精确分割对于前列腺癌放射治疗计划的制定是非常重要的。我们提出了一种使用深度学习进行男性盆腔CT图像分割的全自动工作流程。该架构由2D器官体积定位网络和3D分割网络组成，用于前列腺、膀胱、直肠和股骨头的体积分割。我们使用了一个多通道的2D U-Net，然后是一个3D U-Net，其编码臂用聚合残差网络（称为ResNeXt）进行修改。该模型在包括136名患者的骨盆CT图像数据集上进行了训练和测试。测试结果表明，基于3D U-Net的分割实现了平均（+/- SD）Dice系数值为90（+/- 2.0）%，96（+/- 3.0）%，95（+/- 1.3）%，95（+/-1.5%）和84前列腺、左股骨头、右股骨头、膀胱和直肠分别为（+/- 3.7）%，使用所提出的全自动分割方法。
Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning. We present a fully automated workflow for male pelvic CT image segmentation using deep learning. The architecture consists of a 2D organ volume localization network followed by a 3D segmentation network for volumetric segmentation of prostate, bladder, rectum, and femoral heads. We used a multi-channel 2D U-Net followed by a 3D U-Net with encoding arm modified with aggregated residual networks, known as ResNeXt. The models were trained and tested on a pelvic CT image dataset comprising 136 patients. Test results show that 3D U-Net based segmentation achieves mean (+/- SD) Dice coefficient values of 90 (+/- 2.0)%, 96 (+/- 3.0)%, 95 (+/- 1.3)%, 95 (+/- 1.5)%, and 84 (+/- 3.7)% for prostate, left femoral head, right femoral head, bladder, and rectum, respectively, using the proposed fully automated segmentation method.