Hybrid deep reinforced regression framework for cardio-thoracic ratio measurement
2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)(2020)
摘要
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.
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关键词
Deep Reinforcement Learning,Convolutional Neural Networks,Chest X-ray,Cardio-Thoracic Ratio
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