Multi-modal learning from unpaired images: Application to multi-organ segmentation in CT and MRI
Valindria VV, Pawlowski N, Rajchl M, Lavdas I, Aboagye EO, Rockall AG, Rueckert D, Glocker B (2018)
Publication Type: Conference contribution
Publication year: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2018-January
Pages Range: 547-556
Conference Proceedings Title: Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Event location: Lake Tahoe, NV, USA
ISBN: 9781538648865
Abstract
Convolutional neural networks have been widely used in medical image segmentation. The amount of training data strongly determines the overall performance. Most approaches are applied for a single imaging modality, e.g., brain MRI. In practice, it is often difficult to acquire sufficient training data of a certain imaging modality. The same anatomical structures, however, may be visible in different modalities such as major organs on abdominal CT and MRI. In this work, we investigate the effectiveness of learning from multiple modalities to improve the segmentation accuracy on each individual modality. We study the feasibility of using a dual-stream encoder-decoder architecture to learn modality-independent, and thus, generalisable and robust features. All of our MRI and CT data are unpaired, which means they are obtained from different subjects and not registered to each other. Experiments show that multi-modal learning can improve overall accuracy over modality-specific training. Results demonstrate that information across modalities can in particular improve performance on varying structures such as the spleen.
Involved external institutions
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
Royal Marsden Hospital / The Royal Marsden NHS Foundation Trust
United Kingdom (GB)
United Kingdom (GB)
Royal Marsden Hospital / The Royal Marsden NHS Foundation Trust
United Kingdom (GB)
How to cite
APA:
Valindria, V.V., Pawlowski, N., Rajchl, M., Lavdas, I., Aboagye, E.O., Rockall, A.G.,... Glocker, B. (2018). Multi-modal learning from unpaired images: Application to multi-organ segmentation in CT and MRI. In Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018 (pp. 547-556). Lake Tahoe, NV, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Valindria, Vanya V., et al. "Multi-modal learning from unpaired images: Application to multi-organ segmentation in CT and MRI." Proceedings of the 18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018, Lake Tahoe, NV, USA Institute of Electrical and Electronics Engineers Inc., 2018. 547-556.
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