Deep Reinforcement Learning for Detection of Inner Ear Abnormal Anatomy in Computed Tomography

Abstract

Detection of abnormalities within the inner ear is a challenging task that, if automated, could provide support for the diagnosis and clinical management of various otological disorders. Inner ear malformations are rare and present great anatomical variation, which challenges the design of deep learning frameworks to automate their detection. We propose a framework for inner ear abnormality detection, based on a deep reinforcement learning model for landmark detection trained in normative data only. We derive two abnormality measurements: the first is based on the variability of the predicted configuration of the landmarks in a subspace formed by the point distribution model of the normative landmarks using Procrustes shape alignment and Principal Component Analysis projection. The second measurement is based on the distribution of the predicted Q-values of the model for the last ten states before the landmarks are located. We demonstrate an outstanding performance for this implementation on both an artificial (0.96 AUC) and a real clinical CT dataset of various malformations of the inner ear (0.87 AUC). Our approach could potentially be used to solve other complex anomaly detection problems.

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