Immersive Visualisation In Medical Imaging

The advent of technologies emphasizing on 3-D model reconstruction, shows a tremendous potential for application in medical imaging, specifically in anomaly detection for target organs. Medical practitioners. Bearing this in mind, the objective of our research is to identify extant anomalies within the organ with an aim to reproduce approximate size and relative location of anomaly and to generate a 3-Dimensional model of a target organ with the anomaly based on scans of medical reports to visualize in an immersive environment through Augmented Reality. In this paper, we discuss the chronological order of detecting, segmenting and rendering the anomalies onto our target organ- the brain. We have employed the U-Net architecture for CNN to train on the MICCAI BraTS 2018 dataset for segmenting brain tumours, particularly high grade gliomas. Post segmentation, we discuss the pipeline of our research, with the end results being GLTF and USDZ file formats to view the 3-D model of the brain along with tumour in Augmented Reality. We conclude the paper by discussing the future scope of research on our current work.