Supervised Transfer Learning for Multi Organs 3D Segmentation With Registration Tools for Metal Artifact Reduction in CT Images

Abstract

Radiological diagnostics are undeniably crucial in everyday healthcare. CT scans can sometimes provide misleading clues and delay successive treatment due to artifacts caused by reflected radiation from metallic implants. This work successfully segments multiple organs containing metal implants and discards artifacts using a combination of non-rigid transformations, Scribbles-based segmentation, and a pre-trained auto segmentation model (DynaUnet -Pretrained-Model). The presented transfer learning model combined the benefits of an interactive environment and reduced computational and processing-time costs. The transfer learning model proved high auto segmentation performance for multi-organs with metal implants' presence by decreasing metal artefact's impact on the segmentation process and the achieved segmentation accuracies between 0.9998 for the spleen and 0.9829 for the stomach.