Details
| Original language | English |
|---|---|
| Title of host publication | International Conference on Medical Image Computing and Computer Assisted Intervention |
| Editors | Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz |
| Pages | 54-64 |
| Number of pages | 11 |
| Publication status | Published - 2020 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 12263 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
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.
Keywords
- Endoscopic surgery, Motion estimation, Patient-specific models
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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International Conference on Medical Image Computing and Computer Assisted Intervention. ed. / Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. 2020. p. 54-64 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12263 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Self-Supervised Domain Adaptation for Patient-Specific, Real-Time Tissue Tracking
AU - Ihler, Sontje
AU - Kuhnke, Felix Konstantin
AU - Laves, Max-Heinrich Viktor
AU - Ortmaier, Tobias
N1 - Funding Information: This work has received funding from the European Union as being part of the EFRE OPhonLas project.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Endoscopic surgery
KW - Motion estimation
KW - Patient-specific models
UR - http://www.scopus.com/inward/record.url?scp=85092749274&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59716-0_6
DO - 10.1007/978-3-030-59716-0_6
M3 - Conference contribution
SN - 9783030597153
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 54
EP - 64
BT - International Conference on Medical Image Computing and Computer Assisted Intervention
A2 - Martel, Anne L.
A2 - Abolmaesumi, Purang
A2 - Stoyanov, Danail
A2 - Mateus, Diana
A2 - Zuluaga, Maria A.
A2 - Zhou, S. Kevin
A2 - Racoceanu, Daniel
A2 - Joskowicz, Leo
ER -