TY - GEN
T1 - Neural Registration and Segmentation of White Matter Tracts in Multi-modal Brain MRI
AU - Barzilay, Noa
AU - Nelkenbaum, Ilya
AU - Konen, Eli
AU - Kiryati, Nahum
AU - Mayer, Arnaldo
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Pre-surgical mapping of white matter (WM) tracts requires specific neuroanatomical knowledge and a significant amount of time. Currently, pre-surgical tractography workflows rely on classical registration tools that prospectively align the multiple brain MRI modalities required for the task. Brain lesions and patient motion may challenge the robustness and accuracy of these tool, eventually requiring additional manual intervention. We present a novel neural workflow for 3-D registration and segmentation of WM tracts in multiple brain MRI sequences. The method is applied to pairs of T1-weighted (T1w) and directionally encoded color (DEC) maps. Validation is provided on two different datasets, the Human Connectome Project (HCP) dataset, and a real pre-surgical dataset. The proposed method outperforms the state-of-the-art TractSeg and AGYnet algorithms on both datasets, quantitatively and qualitatively, suggesting its applicability to automatic WM tract mapping in neuro-surgical MRI.
AB - Pre-surgical mapping of white matter (WM) tracts requires specific neuroanatomical knowledge and a significant amount of time. Currently, pre-surgical tractography workflows rely on classical registration tools that prospectively align the multiple brain MRI modalities required for the task. Brain lesions and patient motion may challenge the robustness and accuracy of these tool, eventually requiring additional manual intervention. We present a novel neural workflow for 3-D registration and segmentation of WM tracts in multiple brain MRI sequences. The method is applied to pairs of T1-weighted (T1w) and directionally encoded color (DEC) maps. Validation is provided on two different datasets, the Human Connectome Project (HCP) dataset, and a real pre-surgical dataset. The proposed method outperforms the state-of-the-art TractSeg and AGYnet algorithms on both datasets, quantitatively and qualitatively, suggesting its applicability to automatic WM tract mapping in neuro-surgical MRI.
KW - Brain MRI
KW - Convolutional neural networks
KW - Multi-modal segmentation
KW - Neuro-surgical planning
KW - Registration
KW - Tractography
UR - http://www.scopus.com/inward/record.url?scp=85151145180&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-25066-8_12
DO - 10.1007/978-3-031-25066-8_12
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AN - SCOPUS:85151145180
SN - 9783031250651
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 252
EP - 267
BT - Computer Vision – ECCV 2022 Workshops, Proceedings
A2 - Karlinsky, Leonid
A2 - Michaeli, Tomer
A2 - Nishino, Ko
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th European Conference on Computer Vision, ECCV 2022
Y2 - 23 October 2022 through 27 October 2022
ER -