Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization by Context-Guided Fully Convolutional Networks.
Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization by Context-Guided Fully Convolutional Networks.
复制标题
通过上下文引导的完全卷积网络，关节颅颌面骨分割和具有里程碑意义的数字化。
DOI：
10.1007/978-3-319-66185-8_81
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发表时间：
2017-09
期刊：
影响因子：
--
通讯作者：
Shen D
中科院分区：
文献类型：
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作者：
Zhang J;Liu M;Wang L;Chen S;Yuan P;Li J;Shen SG;Tang Z;Chen KC;Xia JJ;Shen D
从锥束计算机断层扫描(CBCT)图像中生成精确的3D模型是制定颅颌面畸形治疗计划的重要步骤。这一过程通常涉及骨骼分割和里程碑式的数字化。由于解剖地标通常位于分割的骨骼区域的边界上，因此骨骼分割和地标数字化的任务可能高度相关。然而，大多数现有的方法只是将它们视为两个独立的任务，而没有考虑它们之间的内在联系。此外，这些方法通常忽略CBCT图像中的空间上下文信息(即从体素到地标的位移)。为此，我们提出了一种上下文引导的全卷积网络(FCN)，用于关节骨骼分割和标志性数字化。具体地说，我们首先训练FCN学习位移图，以捕捉CBCT图像中的空间上下文信息。利用学习到的位移图作为指导信息，我们进一步开发了一个多任务的FCN来联合执行骨骼分割和地标数字化。我们的方法已经在两个中心的107名受试者上进行了评估，实验结果表明，我们的方法在骨骼分割和标志性数字化方面都优于最先进的方法。
Generating accurate 3D models from cone-beam computed tomography (CBCT) images is an important step in developing treatment plans for patients with craniomaxillofacial (CMF) deformities. This process often involves bone segmentation and landmark digitization. Since anatomical landmarks generally lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly correlated. However, most existing methods simply treat them as two standalone tasks, without considering their inherent association. In addition, these methods usually ignore the spatial context information (i.e., displacements from voxels to landmarks) in CBCT images. To this end, we propose a context-guided fully convolutional network (FCN) for joint bone segmentation and landmark digitization. Specifically, we first train an FCN to learn the displacement maps to capture the spatial context information in CBCT images. Using the learned displacement maps as guidance information, we further develop a multi-task FCN to jointly perform bone segmentation and landmark digitization. Our method has been evaluated on 107 subjects from two centers, and the experimental results show that our method is superior to the state-of-the-art methods in both bone segmentation and landmark digitization.