Farhangi M Mehdi, Dunlap Neal, Amini Amir
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:3405-3408. doi: 10.1109/EMBC.2017.8037587.
The conventional graph cuts technique has been widely used for image segmentation due to its ability to find the global minimum and its ease of implementation. However, it is an intensity-based technique and as a result is limited to segmentation applications where there is significant contrast between the object and the background. We modified the conventional graph cuts method by adding shape prior and motion information. Active shape models (ASM) with signed distance functions were used to capture the shape prior information, preventing unwanted surrounding tissue from becoming part of the segmented object. The optical flow method was used to estimate the local motion and to extend 3D segmentation to 4D by warping a prior shape model through time. The method has been applied to segmentation of whole lung boundary and whole liver boundary from respiratory gated CT data. 4D lung boundary segmentation in five patients, and 4D liver boundary segmentation in five patients were performed and in each case, results were compared with the results from expert-delineated ground truth. 4D segmentation for five phases of CT data took approximately ten minutes on a PC workstation with AMD Phenom II and 32GB of memory. An important by-product is quantitative whole organ volumes from respiratory gated CT from end-inspiration to end-expiration which can be determined with high accuracy.
传统的图割技术因其能够找到全局最小值且易于实现，已被广泛应用于图像分割。然而，它是一种基于强度的技术，因此仅限于目标与背景之间存在显著对比度的分割应用。我们通过添加形状先验和运动信息对传统的图割方法进行了修改。使用具有符号距离函数的主动形状模型（ASM）来捕获形状先验信息，防止不需要的周围组织成为分割对象的一部分。光流方法用于估计局部运动，并通过随时间扭曲先验形状模型将3D分割扩展到4D。该方法已应用于从呼吸门控CT数据中分割全肺边界和全肝边界。对5名患者进行了4D肺边界分割，对5名患者进行了4D肝边界分割，并且在每种情况下，将结果与专家划定的真实情况结果进行了比较。在配备AMD Phenom II和32GB内存的PC工作站上，对CT数据的五个阶段进行4D分割大约需要十分钟。一个重要的副产品是从吸气末到呼气末的呼吸门控CT的定量全器官体积，其可以高精度确定。
