PU-NET DEEP LEARNING ARCHITECTURE FOR GLIOMAS BRAIN TUMOUR SEGMENTATION IN MAGNETIC RESONANCE IMAGES

Abstract

Automatic medical image segmentation is one of the main tasks for many organs and pathology structure delineation. It is also a crucial technique in the posterior clinical examination of brain tumours, like applying radiotherapy or tumour restrictions. Various image segmentation techniques have been proposed and applied to different image types of images. Recently, it has been shown that the deep learning approach accurately segments images, and its implementation is usually straightforward. In this paper, we proposed a novel approach, called PU-net, for automatic brain tumour segmentation in multi-modal magnetic resonance images (MRI) based on deep learning. We introduced an input processing block to a customised fully convolutional network derived from the U-Net network to handle the multi-modal inputs. We performed experiments over the Brats brain tumour dataset collected in 2018 and achieved dice scores of 0.905,0.827, and 0.803 for the whole tumour (WT), tumour core (TC), and enhancing tumour (ET) classes, respectively. This study also provides promising results compared to the traditional machine learning methods, such as support vector machines (SVM), random forest (RF) and other deep learning methods used in this context.