Robust whole-brain segmentation: Application to traumatic brain injury

• Published in: Medical image analysis Vol. 21; no. 1; pp. 40 - 58
• Main Authors: Ledig, Christian, Heckemann, Rolf A., Hammers, Alexander, Lopez, Juan Carlos, Newcombe, Virginia F.J., Makropoulos, Antonios, Lötjönen, Jyrki, Menon, David K., Rueckert, Daniel
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.04.2015
• Subjects: Adult; Algorithms; Artificial Intelligence; AXONAL; Biomedical; Brain - pathology; Brain image; Brain image segmentation; Brain Injuries - pathology; CLASSIFICATION; Clinical Medicine; Computer Science; COST FUNCTION MASKING; DIFFUSION-TENSOR; Engineering; Expectation–maximisation; HIPPOCAMPUS; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; INJURY; Interdisciplinary Applications; Klinisk medicin; LABEL FUSION; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Models, Biological; Models, Statistical; MR-IMAGES; Multi-atlas segmentation; NONRIGID REGISTRATION; Nuclear Medicine & Medical Imaging; Pattern Recognition, Automated - methods; Radiology; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; Traumatic brain injury; VENTRICLE SEGMENTATION; Multi-atlas segmentation; Traumatic brain injury; Magnetic resonance imaging; Expectation–maximisation; Brain image segmentation
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2014.12.003

[Display omitted] •A robust and fully-automatic segmentation framework for MR brain scans is proposed.•A heterogeneous cohort of 125 scans of patients who had sustained TBI is segmented.•The approach compares favourably to the state-of-the-art on benchmark and TBI data.•MRI based biomarkers correlate with outcome-relevant clinical variables in TBI.•Evidence that subcortical structures are particularly affected in TBI is presented. We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called “Multi-Atlas Label Propagation with Expectation–Maximisation based refinement” (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.