Journal
2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018)
Volume -, Issue -, Pages 547-556Publisher
IEEE
DOI: 10.1109/WACV.2018.00066
Keywords
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Funding
- Indonesia Endowment for Education (LPDP) - Indonesia Presidential PhD Scholarship programme
- Microsoft Research PhD Scholarship
- EPSRC Centre for Doctoral Training in High Performance Embedded and Distributed Systems (HiPEDS) [EP/L016796/1]
- Imperial College Research Fellowship
- Efficacy and Mechanism Evaluation (EME) Programme
- MRC
- NIHR [13/122/01]
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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.
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