Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets
Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets
复制标题
使用深度卷积神经网络和时间隐式水平集自动腹部多器官分割
DOI：
10.1007/s11548-016-1501-5
复制
发表时间：
2017-03-01
影响因子：
3
通讯作者：
Kong, Dexing
中科院分区：
文献类型：
--
作者：
Hu, Peijun;Wu, Fa;Kong, Dexing
目的对CT图像进行多器官分割是计算机辅助诊断和手术计划的重要步骤。然而，由放射科医生手工描绘器官是乏味、耗时和难以复制的。为此，我们提出了一种腹部三维CT图像中多器官的全自动分割方法。方法采用深度全卷积神经网络（cnn）进行器官检测和分割，并采用时间隐式多阶段进化方法对其进行进一步细化。首先，利用概率预测图训练三维CNN自动定位和圈定感兴趣的器官；学习到的概率图提供了特定主题的空间先验和后续精细分割的初始化。然后，将图像强度模型、概率先验和不连接区域约束结合到统一的能量泛函中，对多器官分割进行细化；最后，利用一种新的时间隐式多相水平集算法对所提出的能量泛函模型进行有效优化。结果140例腹部CT对肝、脾、双肾四脏器的分割进行了评价。相对于ground truth，肝脏、脾脏和双肾的平均Dice重叠率分别为96.0、94.2和95.4%，所有分节器官的平均对称表面距离小于1.3 mm。一个CT体积的计算时间平均为125秒。所取得的准确性与最先进的方法相比要好得多，效率更高。结论建立并评价了一种腹部CT图像多器官自动分割方法。结果表明，该方法具有较高的有效性、稳健性和高效性，具有临床应用潜力。
PurposeMulti-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images.MethodsThe proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model.ResultsOur method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency.ConclusionA fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.