most citedFighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data

14 citations · 14 across the 2 of their papers we have counts for

collaborators

2 papers

eess.IV2022★ 14 cited

Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data

Michele Svanera, Mattia Savardi, Alberto Signoroni +2

Many clinical and research studies of the human brain require an accurate structural MRI segmentation. While traditional atlas-based methods can be applied to volumes from any acqu…

eess.IV2019

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

Dennis Bontempi, Sergio Benini, Alberto Signoroni +2

Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Cur…