P. R. Singh, S. Gopalakrishnan, I. H. Mien and A. Ambikapathi, "Hybrid Deep Reinforced Regression Framework for Cardio-Thoracic Ratio Measurement," 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, pp. 433-437, doi: 10.1109/ICIP40778.2020.9191287.
Quantitative measurements obtained from medical images
guide clinicians in several use cases but manually obtaining
such measurements are both laborious and subject to interobserver
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.
This research is supported by core funding from the Institute of Infocomm Research