Hybrid Deep Reinforced Regression Framework for Cardio-Thoracic Ratio Measurement
| Autor: | Pranshu Ranjan Singh, ArulMurugan Ambikapathi, Ivan Ho Mien, Saisubramaniam Gopalakrishnan |
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| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Pattern recognition 02 engineering and technology Regression 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Artificial intelligence Ratio measurement business Wingspan |
| Zdroj: | ICIP |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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