Combining multiple expert annotations using semi-supervised learning and graph cuts for Crohn’s disease segmentation

Autor: Mahapatra, Dwarikanath, Schüffler, Peter J., Tielbeek, Jeroen A.W., Puylaert, Carl A.J., Makanyanga, Jesica C., Menys, Alex, Andriantsimiavona, Rado, Stoker, Jaap, Taylor, Stuart A., Vos, Franciscus M., Buhmann, Joachim M., Yoshida, Hiroyuki, Näppi, Janne J., Saini, Sanjay

Publikováno v: Abdominal Imaging: Computational and Clinical Applications-6th International Workshop, ABDI 2014 held in conjunction with MICCAI 2014, 139-147
STARTPAGE=139;ENDPAGE=147;TITLE=Abdominal Imaging
Lecture Notes in Computer Science ISBN: 9783319136912
ABDI@MICCAI

We propose a graph cut (GC) based approach for combining annotations from multiple experts and segmenting Crohns disease (CD) tissues in magnetic resonance (MR) images. Random forest (RF) based semi supervised learning (SSL) predicts missing expert l

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7dcf51481e7544dc1ef88df2393c052c
https://pure.amc.nl/en/publications/combining-multiple-expert-annotations-using-semisupervised-learning-and-graph-cuts-for-crohns-disease-segmentation(39b42cfc-8cd2-4a67-854f-fdc9fcf20a31).html

Semi-automatic bowel wall thickness measurements on MR enterography in patients with Crohn's disease.

Autor: Naziroglu, Robiel E., Puylaert, Carl A.J., Tielbeek, Jeroen A.W., Makanyanga, Jesica, Menys, Alex, Ponsioen, Cyriel Y., Hatzakis, Haralambos, Taylor, Stuart A., Stoker, Jaap, van Vliet, Lucas J., Vos, Frans M.

Publikováno v: British Journal of Radiology; Jun2017, Vol. 90 Issue 1074, p-1, 11p