An end-to-end DL pipeline automates DCE-MRI analysis for brain tumors, introduces a cubic vascular input function model that lowers fitting error, and processes scans in under 3 minutes on one GPU while claiming state-of-the-art accuracy on BraTS and QIBA benchmarks plus 44 clinical cases.
Wu, et al., Brain tumor detection and segmentation in a CRF frame- work with pixel-pairwise affinity and superpixel-level features, Int
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Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
An end-to-end DL pipeline automates DCE-MRI analysis for brain tumors, introduces a cubic vascular input function model that lowers fitting error, and processes scans in under 3 minutes on one GPU while claiming state-of-the-art accuracy on BraTS and QIBA benchmarks plus 44 clinical cases.