计算机科学
人工智能
分割
地标
地图集(解剖学)
脑图谱
正电子发射断层摄影术
图像配准
计算机视觉
模式识别(心理学)
深度学习
核医学
图像(数学)
医学
解剖
作者
Zhaofeng Chen,Tianshuang Qiu,Yang Tian,Hongbo Feng,Yanjun Zhang,Hongkai Wang
标识
DOI:10.1088/1361-6560/abf201
摘要
Abstract Automated brain structures segmentation in positron emission tomography (PET) images has been widely investigated to help brain disease diagnosis and follow-up. To relieve the burden of a manual definition of volume of interest (VOI), automated atlas-based VOI definition algorithms were developed, but these algorithms mostly adopted a global optimization strategy which may not be particularly accurate for local small structures (especially the deep brain structures). This paper presents a PET/CT-based brain VOI segmentation algorithm combining anatomical atlas, local landmarks, and dual-modality information. The method incorporates local deep brain landmarks detected by the Deep Q-Network (DQN) to constrain the atlas registration process. Dual-modality PET/CT image information is also combined to improve the registration accuracy of the extracerebral contour. We compare our algorithm with the representative brain atlas registration methods based on 86 clinical PET/CT images. The proposed algorithm obtained accurate delineation of brain VOIs with an average Dice similarity score of 0.79, an average surface distance of 0.97 mm (sub-pixel level), and a volume recovery coefficient close to 1. The main advantage of our method is that it optimizes both global-scale brain matching and local-scale small structure alignment around the key landmarks, it is fully automated and produces high-quality parcellation of the brain structures from brain PET/CT images.
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