Random forest active learning for AAA thrombus segmentation in computed tomography angiography images

Abstract

Image segmentation of 3D Computed Tomography Angiography (CTA) is affected by a variety of noise conditions that may render ineffective image segmentation procedures that have been developed and validated on a collection of training CTA data when applied on new CTA data. The approach followed in this paper to tackle this problem is to provide an Active Learning based interactive image segmentation system which will allow quick volume segmentation, with minimal intervention of a human operator. Image segmentation is achieved by a Random forest (RF) classifier applied on a set of image features extracted from each voxel and its neighborhood. An initial set of labeled voxels is required to start the process, training an initial RF. The most uncertain unlabeled voxels are shown to the human operator to select some of them for inclusion in the training set, retraining the RF classifier. The approach is applied to the segmentation of the thrombus of Abdominal Aortic Aneurysm (AAA) in CTA data (of patients), showing that the CTA volume can be accurately segmented after few iterations requiring a small labeled data sample.