TY - GEN
T1 - Deep Nested level sets
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
AU - Duan, Jinming
AU - Schlemper, Jo
AU - Bai, Wenjia
AU - Dawes, Timothy J.W.
AU - Bello, Ghalib
AU - Doumou, Georgia
AU - De Marvao, Antonio
AU - O’Regan, Declan P.
AU - Rueckert, Daniel
PY - 2018/9/13
Y1 - 2018/9/13
N2 - In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method explicitly takes into account the image features learned from a deep neural network. To this end, we estimate simultaneous probability maps over region and edge locations in CMR images using a fully convolutional network. Due to the distinct morphology of the heart in patients with PH, these probability maps can then be incorporated in a single nested level set optimisation framework to achieve multi-region segmentation with high efficiency. The proposed method uses an automatic way for level set initialisation and thus the whole optimisation is fully automated. We demonstrate that the proposed deep nested level set (DNLS) method outperforms existing state-of-the-art methods for CMR segmentation in PH patients.
AB - In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method explicitly takes into account the image features learned from a deep neural network. To this end, we estimate simultaneous probability maps over region and edge locations in CMR images using a fully convolutional network. Due to the distinct morphology of the heart in patients with PH, these probability maps can then be incorporated in a single nested level set optimisation framework to achieve multi-region segmentation with high efficiency. The proposed method uses an automatic way for level set initialisation and thus the whole optimisation is fully automated. We demonstrate that the proposed deep nested level set (DNLS) method outperforms existing state-of-the-art methods for CMR segmentation in PH patients.
UR - http://www.scopus.com/inward/record.url?scp=85053849536&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00937-3_68
DO - 10.1007/978-3-030-00937-3_68
M3 - Conference contribution
AN - SCOPUS:85053849536
SN - 9783030009366
T3 - Lecture Notes in Computer Science
SP - 595
EP - 603
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
A2 - Frangi, Alejandro F.
A2 - Schnabel, Julia A.
A2 - Davatzikos, Christos
A2 - Alberola-López, Carlos
A2 - Fichtinger, Gabor
PB - Springer
Y2 - 16 September 2018 through 20 September 2018
ER -