Machine learning based brain tumour segmentation on limited data using local texture and abnormality

Computers in Biology and Medicine
Stijn BonteRoel Van Holen

Abstract

Brain tumour segmentation in medical images is a very challenging task due to the large variety in tumour shape, position, appearance, scanning modalities and scanning parameters. Most existing segmentation algorithms use information from four different MRI-sequences, but since this is often not available, there is need for a method able to delineate the different tumour tissues based on a minimal amount of data. We present a novel approach using a Random Forests model combining voxelwise texture and abnormality features on a contrast-enhanced T1 and FLAIR MRI. We transform the two scans into 275 feature maps. A random forest model next calculates the probability to belong to 4 tumour classes or 5 normal classes. Afterwards, a dedicated voxel clustering algorithm provides the final tumour segmentation. We trained our method on the BraTS 2013 database and validated it on the larger BraTS 2017 dataset. We achieve median Dice scores of 40.9% (low-grade glioma) and 75.0% (high-grade glioma) to delineate the active tumour, and 68.4%/80.1% for the total abnormal region including edema. Our fully automated brain tumour segmentation algorithm is able to delineate contrast enhancing tissue and oedema with high accuracy based only on pos...Continue Reading

Citations

Sep 3, 2019·Frontiers in Oncology·Houman SotoudehGregory K Friedman
Oct 30, 2020·Forensic Science International : Synergy·Carlos A Peña-SolórzanoMatthew R Dimmock
Jun 14, 2021·Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia·Quinlan D BuchlakMassimo Piccardi
Sep 10, 2020·Computers in Biology and Medicine·Kang K YanHerbert H Pang

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