TY - CHAP
T1 - Scalable multimodal convolutional networks for brain tumour segmentation
AU - Fidon, Lucas
AU - Li, Wenqi
AU - Garcia-Peraza-Herrera, Luis C.
AU - Ekanayake, Jinendra
AU - Kitchen, Neil
AU - Ourselin, Sebastien
AU - Vercauteren, Tom
PY - 2017
Y1 - 2017
N2 - Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these models tend to generalise poorly to different imaging modalities than those for which they have been designed, thereby limiting their applications. For example, a network architecture initially designed for brain parcellation of monomodal T1 MRI can not be easily translated into an efficient tumour segmentation network that jointly utilises T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable multimodal deep learning architecture using new nested structures that explicitly leverage deep features within or across modalities. This aims at making the early layers of the architecture structured and sparse so that the final architecture becomes scalable to the number of modalities. We evaluate the scalable architecture for brain tumour segmentation and give evidence of its regularisation effect compared to the conventional concatenation approach.
AB - Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these models tend to generalise poorly to different imaging modalities than those for which they have been designed, thereby limiting their applications. For example, a network architecture initially designed for brain parcellation of monomodal T1 MRI can not be easily translated into an efficient tumour segmentation network that jointly utilises T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable multimodal deep learning architecture using new nested structures that explicitly leverage deep features within or across modalities. This aims at making the early layers of the architecture structured and sparse so that the final architecture becomes scalable to the number of modalities. We evaluate the scalable architecture for brain tumour segmentation and give evidence of its regularisation effect compared to the conventional concatenation approach.
UR - https://www.scopus.com/pages/publications/85029531776
U2 - 10.1007/978-3-319-66179-7_33
DO - 10.1007/978-3-319-66179-7_33
M3 - Conference paper
AN - SCOPUS:85029531776
SN - 9783319661780
VL - 10435
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 285
EP - 293
BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
PB - Springer Verlag
T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Y2 - 11 September 2017 through 13 September 2017
ER -