TY - GEN
T1 - A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
AU - Cerri, Stefano
AU - Hoopes, Andrew
AU - Greve, Douglas N.
AU - Mühlau, Mark
AU - Van Leemput, Koen
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis. The method builds upon an existing cross-sectional method for simultaneous whole-brain and lesion segmentation, introducing subject-specific latent variables to encourage temporal consistency between longitudinal scans. It is very generally applicable, as it does not make any prior assumptions on the scanner, the MRI protocol, or the number and timing of longitudinal follow-up scans. Preliminary experiments on three longitudinal datasets indicate that the proposed method produces more reliable segmentations and detects disease effects better than the cross-sectional method it is based upon.
AB - In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis. The method builds upon an existing cross-sectional method for simultaneous whole-brain and lesion segmentation, introducing subject-specific latent variables to encourage temporal consistency between longitudinal scans. It is very generally applicable, as it does not make any prior assumptions on the scanner, the MRI protocol, or the number and timing of longitudinal follow-up scans. Preliminary experiments on three longitudinal datasets indicate that the proposed method produces more reliable segmentations and detects disease effects better than the cross-sectional method it is based upon.
UR - http://www.scopus.com/inward/record.url?scp=85101556254&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-66843-3_12
DO - 10.1007/978-3-030-66843-3_12
M3 - Conference contribution
AN - SCOPUS:85101556254
SN - 9783030668426
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 119
EP - 128
BT - Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - 3rd International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Kia, Seyed Mostafa
A2 - Mohy-ud-Din, Hassan
A2 - Abdulkadir, Ahmed
A2 - Bass, Cher
A2 - Habes, Mohamad
A2 - Rondina, Jane Maryam
A2 - Tax, Chantal
A2 - Wang, Hongzhi
A2 - Wolfers, Thomas
A2 - Rathore, Saima
A2 - Ingalhalikar, Madhura
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and 2nd International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020
Y2 - 4 October 2020 through 8 October 2020
ER -