Application of Transfer Learning in Medical Imaging

Abstract

The state-of-the-art prostate boundary identification procedures include manual segmentation of prostate gland in MRI images by skilled radiologists, which is painstaking and time-consuming as they go through MRI images slice-by-slice with careful visual inspection. In recent years, several works have been done on implementing deep neural networks to segment images automatically into some particular classes, which is also known as semantic segmentation. Literature says that if the deep neural net model can be trained properly, it can yield higher accuracy than any other algorithms in case of semantic segmentations. This is why this research adopts a pre-trained deep neural network model (VGG19) to perform semantic segmentation of MRI image slices in order to automatically localize the prostate gland and possibly make a 3D model of it for better diagnosis.