Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks.
Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks.
复制标题
使用独特曲线引导的全卷积网络进行盆腔器官分割
DOI：
10.1109/tmi.2018.2867837
复制
发表时间：
2019-03
影响因子：
10.6
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
He K;Cao X;Shi Y;Nie D;Gao Y;Shen D
从CT图像中准确地分割出盆腔器官(即前列腺、膀胱和直肠)是前列腺癌放射治疗的关键。然而，这是一个具有挑战性的任务：1)CT图像软组织对比度低；2)盆腔器官形态和外观变化大。在本文中，我们使用了一种基于两阶段深度学习的方法--一种新颖的独特的曲线制导的全卷积网络(FCN)来解决上述挑战。具体地说，第一阶段是在原始CT图像中进行快速而稳健的器官检测。它被设计为一个粗略的分割网络，为三个盆腔器官提供区域建议。第二阶段是基于区域建议结果对每个器官进行精细分割。为了更好地识别那些难以区分的盆腔器官边界，还引入了一种新的形态表示，即独特的曲线，以帮助更好地进行精确分割。为了实现这一点，在第二阶段，首先利用多任务FCN来分别学习区分曲线和分割图，然后将这两个任务结合起来生成准确的分割图。通过加权最大投票策略生成最终的骨盆三个器官分割结果。我们在一个庞大多样的骨盆CT数据集上进行了详尽的实验，以评估我们所提出的方法。实验结果表明，对于这一具有挑战性的分割任务，我们提出的方法是准确和健壮的，其性能也优于最新的分割方法。
Accurate segmentation of pelvic organs (i.e., prostate, bladder, and rectum) from CT image is crucial for effective prostate cancer radiotherapy. However, it is a challenging task due to: 1) low soft tissue contrast in CT images and 2) large shape and appearance variations of pelvic organs. In this paper, we employ a two-stage deep learning-based method, with a novel distinctive curve-guided fully convolutional network (FCN), to solve the aforementioned challenges. Specifically, the first stage is for fast and robust organ detection in the raw CT images. It is designed as a coarse segmentation network to provide region proposals for three pelvic organs. The second stage is for fine segmentation of each organ, based on the region proposal results. To better identify those indistinguishable pelvic organ boundaries, a novel morphological representation, namely, distinctive curve, is also introduced to help better conduct the precise segmentation. To implement this, in this second stage, a multi-task FCN is initially utilized to learn the distinctive curve and the segmentation map separately and then combine these two tasks to produce accurate segmentation map. The final segmentation results of all three pelvic organs are generated by a weighted max-voting strategy. We have conducted exhaustive experiments on a large and diverse pelvic CT data set for evaluating our proposed method. The experimental results demonstrate that our proposed method is accurate and robust for this challenging segmentation task, by also outperforming the state-of-the-art segmentation methods.