Accurate Localization of Inner Ear Regions of Interests Using Deep Reinforcement Learning

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