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cs.CV2026★ 2 cited
Atlas-Assisted Segment Anything Model for Fetal Brain MRI (FeTal-SAM)
Qi Zeng, Weide Liu, Bo Li +3
This paper presents FeTal-SAM, a novel adaptation of the Segment Anything Model (SAM) tailored for fetal brain MRI segmentation. Traditional deep learning methods often require lar…
cs.CV2026
PathoSyn: Imaging-Pathology MRI Synthesis via Disentangled Deviation Diffusion
Jian Wang, Sixing Rong, Jiarui Xing +2
We present PathoSyn, a unified generative framework for Magnetic Resonance Imaging (MRI) image synthesis that reformulates imaging-pathology as a disentangled additive deviation on…
cs.CV2024
Streamline tractography of the fetal brain in utero with machine learning
Weide Liu, Camilo Calixto, Simon K. Warfield +1
Diffusion-weighted magnetic resonance imaging (dMRI) is the only non-invasive tool for studying white matter tracts and structural connectivity of the brain. These assessments rely…