It is important, yet a challenging procedure, to segment bones and cartilages from knee MR images. In this paper, we propose multi-atlas context forests to first segment bones and then segment cartilages. Specifically, for both the bone and cartilage segmentations, we iteratively train sets of random forests, based on training atlas images, to classify the individual voxels. The random forests rely on (1) the appearance features directly computed from images and also (2) the context features associated with tentative segmentation results, generated by the previous layer of random forest in the iterative framework. To extract context features, multiple atlases (with expert segmentation) are first registered, with the tentative segmentation result of the subject under consideration. Then, the spatial priors of anatomical labels of registered atlases are computed and used to calculate context features of the subject. Note that these multi-atlas context features will be iteratively refined based on the (updated) tentative segmentation result of the subject. As better segmentation result leads to more accurate registration between multiple atlases and the subject, context features will become increasingly more useful for the training of subsequent random forests in the iterative framework. As validated by experiments on the SKI10 dataset, our proposed method can achieve high segmentation accuracy.
|Title of host publication
|Machine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings
|Luping Zhou, Yinghuan Shi, Li Wang, Qian Wang
|Number of pages
|Published - 2015
|6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: 2015 Oct 5 → 2015 Oct 5
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015
|15/10/5 → 15/10/5
Bibliographical notePublisher Copyright:
© Springer International Publishing Switzerland 2015.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)