Multi-task Semi-supervised Learning for Pulmonary Lobe Segmentation
| Autor: | I. Hernandez-Giron, Zhiwei Zhai, Marius Staring, Jingnan Jia, Berend C. Stoel, M. Els Bakker |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Semi-supervised learning 030218 nuclear medicine & medical imaging Task (project management) Machine Learning (cs.LG) 03 medical and health sciences 0302 clinical medicine medicine FOS: Electrical engineering electronic engineering information engineering Preprocessor Leverage (statistics) Segmentation business.industry Deep learning Image and Video Processing (eess.IV) Pattern recognition Image segmentation Electrical Engineering and Systems Science - Image and Video Processing Lobe medicine.anatomical_structure Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | ISBI |
| DOI: | 10.48550/arxiv.2104.11017 |
| Popis: | Pulmonary lobe segmentation is an important preprocessing task for the analysis of lung diseases. Traditional methods relying on fissure detection or other anatomical features, such as the distribution of pulmonary vessels and airways, could provide reasonably accurate lobe segmentations. Deep learning based methods can outperform these traditional approaches, but require large datasets. Deep multi-task learning is expected to utilize labels of multiple different structures. However, commonly such labels are distributed over multiple datasets. In this paper, we proposed a multi-task semi-supervised model that can leverage information of multiple structures from unannotated datasets and datasets annotated with different structures. A focused alternating training strategy is presented to balance the different tasks. We evaluated the trained model on an external independent CT dataset. The results show that our model significantly outperforms single-task alternatives, improving the mean surface distance from 7.174 mm to 4.196 mm. We also demonstrated that our approach is successful for different network architectures as backbones. Comment: 4 pages, to be published in ISBI 2021 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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