Abstract

The introduction of quantitative image analysis has given rise to fields such as radiomics which have been used to predict clinical sequelae. One growing area of interest for analysis is brain tumours, in particular glioblastoma multiforme (GBM). Tumour segmentation is an important step in the pipeline in the analysis of this pathology. Manual segmentation is often inconsistent as it varies between observers. Automated segmentation has been proposed to combat this issue. Methodologies such as convolutional neural networks (CNNs) which are machine learning pipelines modelled on the biological process of neurons (called nodes) and synapses (connections) have been of interest in the literature. We investigate the role of CNNs to segment brain tumours by firstly taking an educational look at CNNs and perform a literature search to determine an example pipeline for segmentation. We then investigate the future use of CNNs by exploring a novel field—radiomics. This examines quantitative features of brain tumours such as shape, texture, and signal intensity to predict clinical outcomes such as survival and response to therapy.

Keypoints

1.Convolutional neural networks simply involve analysing features derived from the image to perform tasks such as segmenting tumours. 2.This initially involves training the network with a manually segmented dataset which then is poised to segment patient images. 3.This has a role in segmentation of brain tumours such as glioblastoma and lower-grade astrocytomas. 4.Segmented images can be further processed to predict clinical sequelae such as survival and response to therapy.