PU-NET Deep Learning Architecture for Gliomas Brain Tumor Segmentation in Magnetic Resonance Images

Abstract

Automatic medical image segmentation is one of the main tasks for many organs and pathology structures delineation. It is also a crucial technique in the posterior clinical examination of brain tumors, like applying radiotherapy or tumor restrictions. Various image segmentation techniques have been proposed and applied to different image types. 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 tumor segmentation in multi-modal magnetic resonance images (MRI). We introduced an input processing block to a customized fully convolutional network derived from the U-Net network to handle the multi-modal inputs. We performed experiments over the Brain Tumor Segmentation (BRATS) dataset collected in 2018 and achieved Dice scores of 90.5%, 82.7%, and 80.3% for the whole tumor, tumor core, and enhancing tumor classes, respectively. This study provides promising results compared to the deep learning methods used in this context.

Keywords

• image segmentation
• deep learning
• u-net
• gliomas
• brain tumor