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Region-based U-net for accelerated training and enhanced precision in deep brain segmentation
March 15, 2024, 4:46 a.m. | Mengyu Li (and for the Alzheimer's Disease Neuroimaging Initiative), Magnus Magnusson (and for the Alzheimer's Disease Neuroimaging Initiative), Thilo
cs.CV updates on arXiv.org arxiv.org
Abstract: Segmentation of brain structures on MRI is the primary step for further quantitative analysis of brain diseases. Manual segmentation is still considered the gold standard in terms of accuracy; however, such data is extremely time-consuming to generate. This paper presents a deep learning-based segmentation approach for 12 deep-brain structures, utilizing multiple region-based U-Nets. The brain is divided into three focal regions of interest that encompass the brainstem, the ventricular system, and the striatum. Next, three …
abstract accuracy analysis arxiv brain cs.cv data deep learning diseases eess.iv generate however mri paper precision quantitative quantitative analysis segmentation standard terms training type
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