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In the medical field, landmark detection in MRI plays an important role inreducing medical technician efforts in tasks like scan planning, imageregistration, etc. First, 88 landmarks spread across the brain anatomy in thethree respective views -- sagittal, coronal, and axial are manually annotated,later guidelines from the expert clinical technicians are takensub-anatomy-wise, for better localization of the existing landmarks, in orderto identify and locate the important atlas landmarks even in oblique scans. Toovercome limited data availability, we implement realistic data augmentation togenerate synthetic 3D volumetric data. We use a modified HighRes3DNet model forsolving brain MRI volumetric landmark detection problem. In order to visuallyexplain our trained model on unseen data, and discern a stronger model from aweaker model, we implement Gradient-weighted Class Activation Mapping(Grad-CAM) which produces a coarse localization map highlighting the regionsthe model is focusing. Our experiments show that the proposed method showsfavorable results, and the overall pipeline can be extended to a variablenumber of landmarks and other anatomies.

Simulating Realistic MRI variations to Improve Deep Learning model and visual explanations using GradCAM