Hierarchic Anatomical Structure Segmentation Guided by Spatial Correlations (AnatSeg-Gspac): VISCERAL Anatomy3
Hierarchic Anatomical Structure Segmentation Guided by Spatial Correlations (AnatSeg-Gspac): VISCERAL Anatomy3
复制标题
空间相关性引导的分层解剖结构分割 (AnatSeg-Gspac)：VISCERAL Anatomy3
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
H. Müller
中科院分区：
文献类型：
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作者：
Oscar Alfonso Jiménez del Toro;Yashin Dicente Cid;A. Depeursinge;H. Müller
医学图像分析技术需要对相应的解剖结构进行初始定位和分割。作为内脏解剖分割基准的一部分，提出了一种由解剖相关性指导的分层多图谱多结构分割方法（AnatSeg-Gspac）。该方法定义了图像的全局对齐，并针对较小的结构局部细化感兴趣的解剖区域。在本文中，在对比增强和非增强计算机断层扫描（CT）扫描的20个解剖结构中，在内脏解剖3基准中评价了该方法。AnatSeg-Gspac在测试集CT扫描的40个可能的结构分数中的19个中获得了最低的平均Hausdorff距离。
Medical image analysis techniques require an initial localization and segmentation of the corresponding anatomical structures. As part of the VISCERAL Anatomy segmentation benchmarks, a hierarchical multi–atlas multi–structure segmentation approach guided by anatomical correlations is proposed (AnatSeg-Gspac). The method defines a global alignment of the images and refines locally the anatomical regions of interest for the smaller structures. In this paper, the method is evaluated in the VISCERAL Anatomy3 benchmark in twenty anatomical structures in both contrast– enhanced and non–enhanced computed tomography (CT) scans. AnatSeg-Gspac obtained the lowest average Hausdorff distance in 19 out of the 40 possible structure scores in the test set CT scans.