The robust delineation of the cochlea and its inner structures combined with the detection of the electrode of a cochlear implant within these structures is essential for envisaging a safer, more individualized, routine image-guided cochlear implant therapy. We present Nautilus—a web-based research platform for automated pre- and post-implantation cochlear analysis. Nautilus delineates cochlear structures from pre-operative clinical CT images by combining deep learning and Bayesian inference approaches. It enables the extraction of electrode locations from a post-operative CT image using convolutional neural networks and geometrical inference. By fusing pre- and post-operative images, Nautilus is able to provide a set of personalized pre- and post-operative metrics that can serve the exploration of clinically relevant questions in cochlear implantation therapy. In addition, Nautilus embeds a self-assessment module providing a confidence rating on the outputs of its pipeline. We present a detailed accuracy and robustness analyses of the tool on a carefully designed dataset. The results of these analyses provide legitimate grounds for envisaging the implementation of image-guided cochlear implant practices into routine clinical workflows.

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Automatic detection of abnormal anatomies or malformations of different structures of the human body is a challenging task that could provide support for clinicians in their daily practice. Compared to normative anatomies, there is a low presence of anatomical abnormalities in patients, and the great variation within malformations make it challenging to design deep learning frameworks for automatic detection. We propose a framework for anatomical abnormality detection, which benefits from using a deep reinforcement learning model for landmark detection trained in normative data. We detect the abnormalities using the variability between the predicted landmarks configurations in a subspace based on a point distribution model of landmarks using Procrustes shape alignment and principal component analysis projection from normative data. We demonstrate the performance of this implementation on clinical CT scans of the inner ear, and show how synthetically created abnormal cochlea anatomy can be detected using the prediction of five landmarks around the cochlea. Our approach shows a Receiver Operating Characteristics (ROC) Area Under The Curve (AUC) of 0.97, and 96% accuracy for the detection of abnormal anatomy on synthetic data.

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.

We propose a novel method for automatic ROI extraction. The method is implemented and tested for isolating the inner ear in full head CT scans. Extracting the ROI with high precision is in this case critical for surgical insertion of cochlear implants. Different parameters, such as CT equipment, image quality, anatomical variation, and the subject's head orientation during scanning make robust ROI extraction challenging. We propose to use state-of-the-art communicative multi-agent reinforcement learning to overcome these difficulties. We specify landmarks specifically designed to robustly extract orientation parameters such that all ROIs have the same orientation and include the relevant anatomy across the dataset. 140 full head CT scans were used to develop and test the ROI extraction pipeline. We report an average overall estimated error for landmark localization of 1.07 mm. Extracted ROI presented an intersection over union of 0.84 and a Dice similarity coefficient of 0.91.