Simulating Realistic MRI variations to Improve Deep Learning model and visual explanations using GradCAM
This work addresses the challenge of reducing medical technician efforts in MRI tasks like scan planning and image registration, though it is incremental as it builds on existing methods like HighRes3DNet and Grad-CAM.
The paper tackled the problem of limited data for brain MRI landmark detection by implementing realistic data augmentation to generate synthetic 3D volumetric data, using a modified HighRes3DNet model and Grad-CAM for visual explanations, showing favorable results that can be extended to other anatomies.
In the medical field, landmark detection in MRI plays an important role in reducing medical technician efforts in tasks like scan planning, image registration, etc. First, 88 landmarks spread across the brain anatomy in the three respective views -- sagittal, coronal, and axial are manually annotated, later guidelines from the expert clinical technicians are taken sub-anatomy-wise, for better localization of the existing landmarks, in order to identify and locate the important atlas landmarks even in oblique scans. To overcome limited data availability, we implement realistic data augmentation to generate synthetic 3D volumetric data. We use a modified HighRes3DNet model for solving brain MRI volumetric landmark detection problem. In order to visually explain our trained model on unseen data, and discern a stronger model from a weaker model, we implement Gradient-weighted Class Activation Mapping (Grad-CAM) which produces a coarse localization map highlighting the regions the model is focusing. Our experiments show that the proposed method shows favorable results, and the overall pipeline can be extended to a variable number of landmarks and other anatomies.