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.

[1]  Loïc Le Folgoc,et al.  Evaluating reinforcement learning agents for anatomical landmark detection , 2019, Medical Image Anal..

[2]  R. Burks,et al.  Does Landmark Selection Affect the Reliability of Tibial Tubercle–Trochlear Groove Measurements Using MRI? , 2012, Clinical orthopaedics and related research.

[3]  Ronan McDermott,et al.  Discrepancy and Error in Radiology: Concepts, Causes and Consequences , 2012, The Ulster medical journal.

[4]  Xiaoou Tang,et al.  Facial Landmark Detection by Deep Multi-task Learning , 2014, ECCV.

[5]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[6]  Daochang Liu,et al.  Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification , 2018, MICCAI.

[7]  Nassir Navab,et al.  X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery , 2018, MICCAI.

[8]  Ching-Wei Wang,et al.  Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms , 2016, Scientific Reports.

[9]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[10]  David Silver,et al.  Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.

[11]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[12]  Michael Kampffmeyer,et al.  Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio , 2018, MICCAI.

[13]  Dorin Comaniciu,et al.  An Artificial Agent for Anatomical Landmark Detection in Medical Images , 2016, MICCAI.

[14]  Hansang Lee,et al.  Cephalometric landmark detection in dental x-ray images using convolutional neural networks , 2017, Medical Imaging.

[15]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[16]  A. Brady.  Error and discrepancy in radiology: inevitable or avoidable? , 2016, Insights into Imaging.

[17]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[18]  C. Danzer.  THE CARDIOTHORACIC RATIO: AN INDEX OF CARDIAC ENLARGEMENT , 1919 .

[19]  Bharadwaj Veeravalli,et al.  CardioXNet: Automated Detection for Cardiomegaly Based on Deep Learning , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  Jun Zhang,et al.  Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks , 2017, IEEE Transactions on Image Processing.