Active learning of Hybrid Extreme Rotation Forests for CTA image segmentation

This paper proposes a Hybrid Extreme Rotation Forest (HERF) classifier for segmentation of 3D Computed Tomography Angiography (CTA) following an Active Learning (AL) approach. The HERF is an ensemble of classifiers composed of Extreme Learning Machines (ELM) and Decision Trees. Training of the HERF includes optimal rotation of random partitions of the feature set aimed to increase diversity. AL follows an strategy of optimal sample selection in order to minimize the number of data samples needed to obtain a required accuracy degree. AL is pertinent for interactive learning processes where a human operator is required to select training samples to enhance the classifier in an iterative process, therefore labeling samples for training may be a time consuming and expensive process. CTA image segmentation is one of such processes, due to the variability in CTA images which hinders the generalization of classifiers trained on one dataset to new datasets. Following an AL strategy, the human operator is presented with a visual selection of pixels whose labeling would be most informative for the classifier. After adding those labeled pixels to the training data, the classifier is retrained. This iteration is repeated until image segmentation quality meets the required level. The approach is applied to the segmentation of the thrombus in CTA data of Abdominal Aortic Aneurysm (AAA) patients, showing that the structures of interest in CTA volume can be accurately segmented after a few iterations using a small data sample.