TY - GEN
T1 - Multi-scale neural network for automatic segmentation of ischemic strokes on acute perfusion images
AU - Lucas, Christian
AU - Kemmling, Andre
AU - Mamlouk, Amir Madany
AU - Heinrich, Mattias P.
N1 - Funding Information:
This work was supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany’s Excellence Initiative [DFG GSC 235/2]. We would also like to thank Nvidia Corporation for their support by providing us with a Titan Xp graphics card.
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - Perfusion imaging is very important for early assessment of strokes due to its ability to measure blood flow, transition times and dispersion. Deep learning approaches are able to perform automatic segmentations, but have had limited accuracy so far for clinically important small lesions. We present an extension to the popular U-Net architecture that concatenates higher-level features and improves their propagation throughout the network using additional skip connections. The new method is evaluated on public perfusion datasets and achieves substantially improved accuracy (33% lower surface distance than U-Net) and robustness in particular for smaller stroke lesions that are difficult to detect.
AB - Perfusion imaging is very important for early assessment of strokes due to its ability to measure blood flow, transition times and dispersion. Deep learning approaches are able to perform automatic segmentations, but have had limited accuracy so far for clinically important small lesions. We present an extension to the popular U-Net architecture that concatenates higher-level features and improves their propagation throughout the network using additional skip connections. The new method is evaluated on public perfusion datasets and achieves substantially improved accuracy (33% lower surface distance than U-Net) and robustness in particular for smaller stroke lesions that are difficult to detect.
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85048097151&origin=inward&txGid=a1d389db14d50bcb01b62c8a032e564f
U2 - 10.1109/ISBI.2018.8363767
DO - 10.1109/ISBI.2018.8363767
M3 - Conference contribution
SN - 978-1-5386-3635-0
SN - 978-1-5386-3637-4
VL - 2018-April
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1118
EP - 1121
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
PB - IEEE
T2 - 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Y2 - 4 April 2018 through 7 April 2018
ER -
