Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations
Bai W, Suzuki H, Qin C, Tarroni G, Oktay O, Matthews PM, Rueckert D (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11073 LNCS
Pages Range: 586-594
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030009366
DOI: 10.1007/978-3-030-00937-3_67
Abstract
Segmentation of image sequences is an important task in medical image analysis, which enables clinicians to assess the anatomy and function of moving organs. However, direct application of a segmentation algorithm to each time frame of a sequence may ignore the temporal continuity inherent in the sequence. In this work, we propose an image sequence segmentation algorithm by combining a fully convolutional network with a recurrent neural network, which incorporates both spatial and temporal information into the segmentation task. A key challenge in training this network is that the available manual annotations are temporally sparse, which forbids end-to-end training. We address this challenge by performing non-rigid label propagation on the annotations and introducing an exponentially weighted loss function for training. Experiments on aortic MR image sequences demonstrate that the proposed method significantly improves both accuracy and temporal smoothness of segmentation, compared to a baseline method that utilises spatial information only. It achieves an average Dice metric of 0.960 for the ascending aorta and 0.953 for the descending aorta.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Bai, W., Suzuki, H., Qin, C., Tarroni, G., Oktay, O., Matthews, P.M., & Rueckert, D. (2018). Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations. In Alejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 586-594). Granada, ESP: Springer Verlag.
MLA:
Bai, Wenjia, et al. "Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations." Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Alejandro F. Frangi, Gabor Fichtinger, Julia A. Schnabel, Carlos Alberola-López, Christos Davatzikos, Springer Verlag, 2018. 586-594.
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