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• W3136236906 startingPage "095003" @default.
• W3136236906 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." @default.
• W3136236906 created "2021-03-29" @default.
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• W3136236906 date "2021-04-23" @default.
• W3136236906 modified "2023-10-06" @default.
• W3136236906 title "Automated brain structures segmentation from PET/CT images based on landmark-constrained dual-modality atlas registration" @default.
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• W3136236906 doi "https://doi.org/10.1088/1361-6560/abf201" @default.
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• W3136236906 hasPublicationYear "2021" @default.
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