Multiple-organ segmentation by graph cuts with supervoxel nodes
Multiple-organ segmentation by graph cuts with supervoxel nodes
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DOI：
10.23919/mva.2017.7986891
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发表时间：
2017-05
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影响因子：
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通讯作者：
Toshiya Takaoka;Yoshihiko Mochizuki;H. Ishikawa
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文献类型：
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作者：
Toshiya Takaoka;Yoshihiko Mochizuki;H. Ishikawa
医学成像技术的进步使医生能够更详细地直接观察患者的身体。然而，由于只能直接看到横截面图像，因此必须将体积分割为器官，以便将其形状视为器官边界表面的 3D 图形。分割对于诊断的定量测量也很重要。在这里，我们介绍了一种新颖的更高精度方法，使用医学图像（例如 CT 扫描图像）中的图形切割来分割多个器官。我们利用超级体素而不是体素作为分割单位，即图形模型中的节点，并设计能量函数以相应地最小化。我们利用 SLIC 超级体素算法，并通过与地面实况相比的能量最小化来验证我们的分割算法的性能。
Improvement in medical imaging technologies has made it possible for doctors to directly look into patients' bodies in ever finer details. However, since only the cross-sectional image can be directly seen, it is essential to segment the volume into organs so that their shape can be seen as 3D graphics of the organ boundary surfaces. Segmentation is also important for quantitative measurement for diagnosis. Here, we introduce a novel higher-precision method to segment multiple organs using graph cuts within medical images such as CT-scanned images. We utilize super voxels instead of voxels as the units of segmentation, i.e., the nodes in the graphical model, and design the energy function to minimize accordingly. We utilize SLIC super voxel algorithm and verify the performance of our segmentation algorithm by energy minimization comparing to the ground truth.