Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan.
School of Information Science, Aichi Institute of Technology, 1247 Yachigusa, Yakusa-cho, Toyota, Aichi, Japan.
Int J Comput Assist Radiol Surg. 2019 Dec;14(12):2069-2081. doi: 10.1007/s11548-019-02062-5. Epub 2019 Sep 6.
The purpose of this paper is to present a fully automated abdominal artery segmentation method from a CT volume. Three-dimensional (3D) blood vessel structure information is important for diagnosis and treatment. Information about blood vessels (including arteries) can be used in patient-specific surgical planning and intra-operative navigation. Since blood vessels have large inter-patient variations in branching patterns and positions, a patient-specific blood vessel segmentation method is necessary. Even though deep learning-based segmentation methods provide good segmentation accuracy among large organs, small organs such as blood vessels are not well segmented. We propose a deep learning-based abdominal artery segmentation method from a CT volume. Because the artery is one of small organs that is difficult to segment, we introduced an original training sample generation method and a three-plane segmentation approach to improve segmentation accuracy. METHOD : Our proposed method segments abdominal arteries from an abdominal CT volume with a fully convolutional network (FCN). To segment small arteries, we employ a 2D patch-based segmentation method and an area imbalance reduced training patch generation (AIRTPG) method. AIRTPG adjusts patch number imbalances between patches with artery regions and patches without them. These methods improved the segmentation accuracies of small artery regions. Furthermore, we introduced a three-plane segmentation approach to obtain clear 3D segmentation results from 2D patch-based processes. In the three-plane approach, we performed three segmentation processes using patches generated on axial, coronal, and sagittal planes and combined the results to generate a 3D segmentation result. RESULTS : The evaluation results of the proposed method using 20 cases of abdominal CT volumes show that the averaged F-measure, precision, and recall rates were 87.1%, 85.8%, and 88.4%, respectively. This result outperformed our previous automated FCN-based segmentation method. Our method offers competitive performance compared to the previous blood vessel segmentation methods from 3D volumes. CONCLUSIONS : We developed an abdominal artery segmentation method using FCN. The 2D patch-based and AIRTPG methods effectively segmented the artery regions. In addition, the three-plane approach generated good 3D segmentation results.
本文旨在提出一种从 CT 体数据中自动分割腹部动脉的方法。三维（3D）血管结构信息对于诊断和治疗至关重要。血管（包括动脉）的信息可用于特定于患者的手术规划和术中导航。由于血管在分支模式和位置上具有较大的个体间差异，因此需要一种特定于患者的血管分割方法。尽管基于深度学习的分割方法在大型器官中提供了良好的分割精度，但小型器官（如血管）的分割效果并不理想。我们提出了一种基于深度学习的从 CT 体数据中分割腹部动脉的方法。由于动脉是难以分割的小器官之一，因此我们引入了原始训练样本生成方法和三平面分割方法，以提高分割精度。
我们提出的方法使用全卷积网络（FCN）从腹部 CT 体数据中分割腹部动脉。为了分割小动脉，我们采用了基于 2D 补丁的分割方法和一种区域不平衡减少训练补丁生成（AIRTPG）方法。AIRTPG 调整了带有动脉区域的补丁和没有动脉区域的补丁之间的补丁数量不平衡。这些方法提高了小动脉区域的分割精度。此外，我们引入了三平面分割方法，从 2D 基于补丁的过程中获得清晰的 3D 分割结果。在三平面方法中，我们使用在轴位、冠状位和矢状位生成的补丁进行三次分割过程，并结合结果生成 3D 分割结果。
使用 20 例腹部 CT 体数据评估提出的方法的结果表明，平均 F 度量、精度和召回率分别为 87.1%、85.8%和 88.4%。这一结果优于我们之前的基于自动化 FCN 的分割方法。与之前从 3D 体数据中分割血管的方法相比，我们的方法具有竞争力的性能。
我们开发了一种基于 FCN 的腹部动脉分割方法。基于 2D 补丁的方法和 AIRTPG 方法有效地分割了动脉区域。此外，三平面方法生成了良好的 3D 分割结果。
