Aghdasi Nava, Li Yangming, Berens Angelique, Harbison Richard A, Moe Kris S, Hannaford Blake
University of Washington, Department of Electrical Engineering, Seattle, Washington, United States.
University of Washington, Department of Otolaryngology, Head and Neck Surgery, Seattle, Washington, United States.
J Med Imaging (Bellingham). 2017 Jul;4(3):034501. doi: 10.1117/1.JMI.4.3.034501. Epub 2017 Jul 22.
We present a fully automatic method for segmenting orbital structures (globes, optic nerves, and extraocular muscles) in CT images. Prior anatomical knowledge, such as shape, intensity, and spatial relationships of organs and landmarks, were utilized to define a volume of interest (VOI) that contains the desired structures. Then, VOI was used for fast localization and successful segmentation of each structure using predefined rules. Testing our method with 30 publicly available datasets, the average Dice similarity coefficient for right and left sides of [0.81, 0.79] eye globes, [0.72, 0.79] optic nerves, and [0.73, 0.76] extraocular muscles were achieved. The proposed method is accurate, efficient, does not require training data, and its intuitive pipeline allows the user to modify or extend to other structures.
我们提出了一种在CT图像中分割眼眶结构（眼球、视神经和眼外肌）的全自动方法。利用先前的解剖学知识，如器官和地标点的形状、强度及空间关系，来定义一个包含所需结构的感兴趣体积（VOI）。然后，使用预定义规则，将VOI用于每个结构的快速定位和成功分割。用30个公开可用数据集测试我们的方法，获得了眼球左右两侧的平均骰子相似系数为[0.81, 0.79]，视神经为[0.72, 0.79]，眼外肌为[0.73, 0.76]。所提出的方法准确、高效，不需要训练数据，其直观的流程允许用户修改或扩展到其他结构。
