Automatic Threshold Detection for Classification of Lesions

Series of brain scans

Total brain white matter lesion volume is the most widely established MRI outcome measure in studies of multiple sclerosis. To estimate white matter lesion volume, there are a number of automatic segmentation methods available. Even with a number of automatic options, manual delineation remains the gold standard approach. Automatic approaches, including MIMoSA, often yield a probability map to which a threshold is applied to create binary lesion segmentation masks. Few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. To overcome some of these issues, Alessandra Valcarcel developed and validated an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense white matter lesions. Valcarcel et al. (2020)


The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) consists of a group of statisticians studying etiology and clinical practice through medical imaging. 


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