Self-Supervised Domain Adaptation for Patient-Specific, Real-Time Tissue Tracking
| Autor: | Max-Heinrich Laves, Sontje Ihler, Felix Kuhnke, Tobias Ortmaier |
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| Rok vydání: | 2020 |
| Předmět: |
Domain adaptation
Tissue tracking Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Optical flow Pattern recognition 02 engineering and technology Patient specific Tracking (particle physics) Motion (physics) 030218 nuclear medicine & medical imaging Task (project management) 03 medical and health sciences 0302 clinical medicine Motion estimation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597153 MICCAI (3) |
| Popis: | Estimating tissue motion is crucial to provide automatic motion stabilization and guidance during surgery. However, endoscopic images often lack distinctive features and fine tissue deformation can only be captured with dense tracking methods like optical flow. To achieve high accuracy at high processing rates, we propose fine-tuning of a fast optical flow model to an unlabeled patient-specific image domain. We adopt multiple strategies to achieve unsupervised fine-tuning. First, we utilize a teacher-student approach to transfer knowledge from a slow but accurate teacher model to a fast student model. Secondly, we develop self-supervised tasks where the model is encouraged to learn from different but related examples. Comparisons with out-of-the-box models show that our method achieves significantly better results. Our experiments uncover the effects of different task combinations. We demonstrate that unsupervised fine-tuning can improve the performance of CNN-based tissue tracking and opens up a promising future direction. |
| Databáze: | OpenAIRE |
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