Active Learning with Bootstrapped Dendritic Classifier applied to medical image segmentation

• Bootstrapped Dendritic Classifier for image segmentation.
• Active Learning of BDC training interactive creation of learning sample data.
• Validation on Abdominal Aortic Aneurysm data.
• Generalization results obtained are state-of-the-art.

We perform the segmentation of medical images following an Active Learning approach that allows quick interactive segmentation minimizing the requirements for intervention of the human operator. The basic classifier is the Bootstrapped Dendritic Classifier (BDC), which combine the output of an ensemble of weak Dendritic Classifiers by majority voting. Weak Dendritic Classifiers are trained on bootstrapped samples of the train data setting a limit on the number of dendrites. We validate the approach on the segmentation of the thrombus in 3D Computed Tomography Angiography (CTA) data of Abdominal Aortic Aneurysm (AAA) patients simulating the human oracle by the provided ground truth. The generalization results in terms of accuracy and true positive ratio of the classification of the entire volume by the classifier trained on one slice confirm that the approach is worth its consideration for clinical practice.