Segmentation with Incremental Classifiers

TL;DR: This paper proposes an alternative approach to extract and encode the physician’s expertise that relies on a specific classification method that incrementally extracts information from groups of pixels in the images.

Abstract: 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.