Surface Muscle Segmentation Using 3D U-Net Based on Selective Voxel Patch Generation in Whole-Body CT Images
Surface Muscle Segmentation Using 3D U-Net Based on Selective Voxel Patch Generation in Whole-Body CT Images
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DOI：
10.3390/app10134477
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发表时间：
2020-07-01
影响因子：
2.7
通讯作者：
Fujita, Hiroshi
中科院分区：
文献类型：
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作者：
Kamiya, Naoki;Oshima, Ami;Fujita, Hiroshi
本研究的目的是开发和验证一种自动分割方法的表面肌肉使用三维（3D）的U-Net的基础上选择体素补丁从全身计算机断层扫描（CT）图像。我们的方法定义了一个体素补丁（VP）作为输入图像，其中包括56个切片，从整个切片中以相等的间隔选择。在培训中，每个病例使用一个VP。在测试中，根据测试用例中的切片数量创建多个VP。然后对每个VP进行分割，并合并每个VP的结果。所提出的方法取得了分割精度平均骰子系数为0.900的8例。虽然在内脏器官附近的肌肉和小肌肉区域中仍然存在挑战，但VP对于使用具有有限注释数据的全身CT图像的表面肌肉分割是有用的。本研究的局限性在于仅限于肌肉萎缩性疾病的病例。未来的研究应该解决所提出的方法是否适用于其他模式或使用不同成像范围的数据。
This study aimed to develop and validate an automated segmentation method for surface muscles using a three-dimensional (3D) U-Net based on selective voxel patches from whole-body computed tomography (CT) images. Our method defined a voxel patch (VP) as the input images, which consisted of 56 slices selected at equal intervals from the whole slices. In training, one VP was used for each case. In the test, multiple VPs were created according to the number of slices in the test case. Segmentation was then performed for each VP and the results of each VP merged. The proposed method achieved a segmentation accuracy mean dice coefficient of 0.900 for 8 cases. Although challenges remain in muscles adjacent to visceral organs and in small muscle areas, VP is useful for surface muscle segmentation using whole-body CT images with limited annotation data. The limitation of our study is that it is limited to cases of muscular disease with atrophy. Future studies should address whether the proposed method is effective for other modalities or using data with different imaging ranges.