Deep-Learning Based Automatic Determination of Cardiac Planes in Survey MRI Data

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Submitted a paper to the MEDICON 2023 conference as the first author. Developed a deep learning model based on the V-Net architecture for automatic inference of the radiological planes of the heart from 3D MRI sequences. This model improves the accuracy and efficiency of medical imaging by generating heatmaps of probable plane positions, enabling faster and more effective diagnosis and treatment. Demonstrated the network's capability to accurately locate cardiac landmarks, even with anisotropic voxel data.

Machine Learning Python PyTorch Tomography Computer vision Conference paper

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