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Deep learning-based medical image segmentation with limited labels.
- Source :
-
Physics in medicine and biology [Phys Med Biol] 2020 Nov 20; Vol. 65 (23). Date of Electronic Publication: 2020 Nov 20. - Publication Year :
- 2020
-
Abstract
- Deep learning (DL)-based auto-segmentation has the potential for accurate organ delineation in radiotherapy applications but requires large amounts of clean labeled data to train a robust model. However, annotating medical images is extremely time-consuming and requires clinical expertise, especially for segmentation that demands voxel-wise labels. On the other hand, medical images without annotations are abundant and highly accessible. To alleviate the influence of the limited number of clean labels, we propose a weakly supervised DL training approach using deformable image registration (DIR)-based annotations, leveraging the abundance of unlabeled data. We generate pseudo-contours by utilizing DIR to propagate atlas contours onto abundant unlabeled images and train a robust DL-based segmentation model. With 10 labeled TCIA dataset and 50 unlabeled CT scans from our institution, our model achieved Dice similarity coefficient of 87.9%, 73.4%, 73.4%, 63.2% and 61.0% on mandible, left & right parotid glands and left & right submandibular glands of TCIA test set and competitive performance on our institutional clinical dataset and a third party (PDDCA) dataset. Experimental results demonstrated the proposed method outperformed traditional multi-atlas DIR methods and fully supervised limited data training and is promising for DL-based medical image segmentation application with limited annotated data.<br /> (© 2020 Institute of Physics and Engineering in Medicine.)
Details
- Language :
- English
- ISSN :
- 1361-6560
- Volume :
- 65
- Issue :
- 23
- Database :
- MEDLINE
- Journal :
- Physics in medicine and biology
- Publication Type :
- Academic Journal
- Accession number :
- 33086205
- Full Text :
- https://doi.org/10.1088/1361-6560/abc363