Automated brain structures segmentation from PET/CT images based on landmark-constrained dual-modality atlas registration. (23rd April 2021)

• Record Type: Journal Article
• Authors: Chen, Zhaofeng; Qiu, Tianshuang; Tian, Yang; Feng, Hongbo; Zhang, Yanjun; Wang, Hongkai
• Is Part Of: Physics in medicine & biology. Volume 66:Number 9(2021)
• Publication Date: 2021-04-23
• Subjects: positron emission tomography (PET) -- brain structure segmentation -- brain atlas -- anatomical landmark -- atlas registration; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/abf201
• Languages: English
• ISSNs: 0031-9155

Abstract

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.