TY - GEN
T1 - Automatic Segmentation of White Matter Tracts Using Multiple Brain MRI Sequences
AU - Nelkenbaum, Ilya
AU - Tsarfaty, Galia
AU - Kiryati, Nahum
AU - Konen, Eli
AU - Mayer, Arnaldo
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - White matter tractography mapping is a must in neuro-surgical planning and navigation to minimize risks of iatrogenic damages. Clinical tractography pipelines still require time consuming manual operations and significant neuro-anatomical expertise, to accurately seed the tracts and remove tractography outliers. The automatic segmentation of white matter (WM) tracts using deep neural networks has been recently demonstrated. However, most of the works in this area use a single brain MRI sequence, whereas neuro-radiologists rely on 2 or more MRI sequences, e.g. T1w and the principal direction of diffusion (PDD), for pre-surgical WM mapping. In this work, we propose a novel neural architecture for the automatic segmentation of white matter tracts by fusing multiple MRI sequences. The proposed method is demonstrated and validated on joint T1w and PDD input sequences. It is shown to compare favorably against state-of-the art methods (Vnet, TractSeg) on the Human Connectome Project (HCP) brain scans dataset for clinically important WM tracts.
AB - White matter tractography mapping is a must in neuro-surgical planning and navigation to minimize risks of iatrogenic damages. Clinical tractography pipelines still require time consuming manual operations and significant neuro-anatomical expertise, to accurately seed the tracts and remove tractography outliers. The automatic segmentation of white matter (WM) tracts using deep neural networks has been recently demonstrated. However, most of the works in this area use a single brain MRI sequence, whereas neuro-radiologists rely on 2 or more MRI sequences, e.g. T1w and the principal direction of diffusion (PDD), for pre-surgical WM mapping. In this work, we propose a novel neural architecture for the automatic segmentation of white matter tracts by fusing multiple MRI sequences. The proposed method is demonstrated and validated on joint T1w and PDD input sequences. It is shown to compare favorably against state-of-the art methods (Vnet, TractSeg) on the Human Connectome Project (HCP) brain scans dataset for clinically important WM tracts.
KW - AGYnet
KW - Convolutional neural networks
KW - DTI
KW - attention gate
KW - multimodal segmentation
KW - segmentation
KW - tractography
KW - white matter
UR - http://www.scopus.com/inward/record.url?scp=85085856132&partnerID=8YFLogxK
U2 - 10.1109/ISBI45749.2020.9098454
DO - 10.1109/ISBI45749.2020.9098454
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AN - SCOPUS:85085856132
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 368
EP - 371
BT - ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Y2 - 3 April 2020 through 7 April 2020
ER -