MedVis Suite: A Framework for MRI Visualization and UNet-Based Bone Segmentation with In-Depth Evaluation

Mengyuan Liu, Di Zhang, Yixiao Chen, Tianchou Gong, Hans Kainz, Seungmoon Song, and Jeongkyu Lee

This study introduces MedVis Suite, a framework developed to address key challenges in medical image analysis using MRI scans. MedVis Suite integrates advanced machine learning techniques, including a U-Net-based segmentation model optimized for bone segmentation, and 3D reconstruction capabilities. An in-depth evaluation of a U-Net-based model for bone segmentation is performed across anatomical planes, optimizing both loss functions and image scales. The axial view showed the highest performance with a Dice score of 0.91 using the baseline model, while the combination of Dice loss and boundary loss produced the best results. MedVis Suite offers significant potential to enhance medical image analysis, improve segmentation accuracy, and provide more comprehensive visualizations for clinical use. Future research will focus
on validating MedVis Suite across diverse datasets and clinical applications, with the integration of image preprocessing techniques and fine-tuning strategies to further enhance the U-Net-based segmentation model.