HYBRID DEEP REINFORCED REGRESSION FRAMEWORK FOR CARDIO-THORACIC RATIO MEASUREMENT

Quantitative measurements obtained from medical images guide clinicians in several use cases but manually obtaining such measurements are both laborious and subject to inter-observer variations. We develop a hybrid deep reinforced regression framework to robustly measure the Cardio-Thoracic ratio (CTR) from Chest X-ray (CXR) images, thereby directly identifying the presence of Cardiomegaly. The proposed hybrid framework initially employs a CNN based Regressor on pre-processed images to obtain approximate critical points. As the actual critical points are based on human expert's experience and subject to labeling uncertainties, a deep reinforcement learning (deep RL) approach is specifically designed to fine-tune estimated regression points from the CNN Regressor. The final regressed points are then used to measure CTR. Wingspan and ChestX-ray8 datasets are used for validating the proposed framework. The proposed framework shows generalization ability on ChestX-ray8 and outperforms the state-of-the-art results on Wingspan.