Segmentation with Incremental Classifiers
Radiotherapy treatment planning requires physicians to delineate the target volumes and organs at risk on 3D images of the patient. This segmentation task consumes a lot of time and can be partly automated with atlases (reference images segmented by experts). To segment any new image, the atlas is non-rigidly registered and the organ contours are then transferred. In practice, this approach suffers from the current limitations of non-rigid registration. We propose an alternative approach to extract and encode the physician’s expertise. It relies on a specific classification method that incrementally extracts information from groups of pixels in the images. The incremental nature of the process allows us to extract features that depend on partial classification results but also convey richer information. This paper is a first investigation of such an incremental scheme, illustrated with experiments on artificial images.
KeywordsRandom Forest Class Label Active Contour Feature Ranking Radiotherapy Treatment Planning
- 1.Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection (1979)Google Scholar
- 2.Beucher, S., Meyer, F.: The morphological approach to segmentation: the watershed transformation. Optical Engineering-New York-Marcel Dekker Incorporated 34, 433–433 (1992)Google Scholar
- 3.Bondiau, P., Malandain, G., Chanalet, S., Marcy, P., Habrand, J., Fauchon, F., Paquis, P., Courdi, A., Commowick, O., Rutten, I., et al.: Atlas-based automatic segmentation of mr images: validation study on the brainstem in radiotherapy context. International Journal of Radiation Oncology* Biology* Physics 61(1), 289–298 (2005)CrossRefGoogle Scholar
- 8.Joachims, T.: Making large scale svm learning practical (1999)Google Scholar