Zhu Ruiyun, Oda Masahiro, Hayashi Yuichiro, Kitasaka Takayuki, Misawa Kazunari, Fujiwara Michitaka, Mori Kensaku
Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan.
Information Technology Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan.
Int J Comput Assist Radiol Surg. 2025 Jan;20(1):77-87. doi: 10.1007/s11548-024-03215-x. Epub 2024 Sep 12.
Accurate segmentation of tubular structures is crucial for clinical diagnosis and treatment but is challenging due to their complex branching structures and volume imbalance. The purpose of this study is to propose a 3D deep learning network that incorporates skeleton information to enhance segmentation accuracy in these tubular structures.
Our approach employs a 3D convolutional network to extract 3D tubular structures from medical images such as CT volumetric images. We introduce a skeleton-guided module that operates on extracted features to capture and preserve the skeleton information in the segmentation results. Additionally, to effectively train our deep model in leveraging skeleton information, we propose a sigmoid-adaptive Tversky loss function which is specifically designed for skeleton segmentation.
We conducted experiments on two distinct 3D medical image datasets. The first dataset consisted of 90 cases of chest CT volumetric images, while the second dataset comprised 35 cases of abdominal CT volumetric images. Comparative analysis with previous segmentation approaches demonstrated the superior performance of our method. For the airway segmentation task, our method achieved an average tree length rate of 93.0%, a branch detection rate of 91.5%, and a precision rate of 90.0%. In the case of abdominal artery segmentation, our method attained an average precision rate of 97.7%, a recall rate of 91.7%, and an F-measure of 94.6%.
We present a skeleton-guided 3D convolutional network to segment tubular structures from 3D medical images. Our skeleton-guided 3D convolutional network could effectively segment small tubular structures, outperforming previous methods.
管状结构的准确分割对于临床诊断和治疗至关重要，但由于其复杂的分支结构和体积不平衡，分割具有挑战性。本研究的目的是提出一种结合骨架信息的三维深度学习网络，以提高这些管状结构的分割准确性。
我们的方法采用三维卷积网络从医学图像（如CT体积图像）中提取三维管状结构。我们引入了一个骨架引导模块，该模块对提取的特征进行操作，以在分割结果中捕获和保留骨架信息。此外，为了在利用骨架信息方面有效地训练我们的深度模型，我们提出了一种sigmoid自适应Tversky损失函数，该函数专门为骨架分割而设计。
我们在两个不同的三维医学图像数据集上进行了实验。第一个数据集由90例胸部CT体积图像组成，而第二个数据集包含35例腹部CT体积图像。与先前分割方法的对比分析证明了我们方法的优越性能。对于气道分割任务，我们的方法实现了平均树长率为93.0%，分支检测率为91.5%，准确率为90.0%。在腹部动脉分割的情况下，我们的方法达到了平均准确率为97.7%，召回率为91.7%，F值为94.6%。
我们提出了一种骨架引导的三维卷积网络，用于从三维医学图像中分割管状结构。我们的骨架引导三维卷积网络能够有效地分割小的管状结构，优于先前的方法。
