Multi-class semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning – an algorithm development and multi-centre validation study
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Background CT is the most common imaging modality in traumatic brain injury (TBI). However, its conventional use
requires expert clinical interpretation and does not provide detailed quantitative outputs, which may have prognostic
importance. We aimed to use deep learning to reliably and efficiently quantify and detect different lesion types.
Methods Patients were recruited between Dec 9, 2014, and Dec 17, 2017, in 60 centres across Europe. We trained and
validated an initial convolutional neural network (CNN) on expert manual segmentations (dataset 1). This CNN was
used to automatically segment a new dataset of scans, which we then corrected manually (dataset 2). From this
dataset, we used a subset of scans to train a final CNN for multiclass, voxel-wise segmentation of lesion types. The
performance of this CNN was evaluated on a test subset. Performance was measured for lesion volume quantification,
lesion progression, and lesion detection and lesion volume classification. For lesion detection, external validation was
done on an independent set of 500 patients from India.
Findings 98 scans from one centre were included in dataset 1. Dataset 2 comprised 839 scans from 38 centres:
184 scans were used in the training subset and 655 in the test subset. Compared with manual reference, CNN-derived
lesion volumes showed a mean difference of 0·86 mL (95% CI –5·23 to 6·94) for intraparenchymal haemorrhage,
1·83 mL (–12·01 to 15·66) for extra-axial haemorrhage, 2·09 mL (–9·38 to 13·56) for perilesional oedema, and
0·07 mL (–1·00 to 1·13) for intraventricular haemorrhage.
Interpretation We show the ability of a CNN to separately segment, quantify, and detect multiclass haemorrhagic
lesions and perilesional oedema. These volumetric lesion estimates allow clinically relevant quantification of
lesion burden and progression, with potential applications for personalised treatment strategies and clinical research
in TBI.
Funding European Union 7th Framework Programme, Hannelore Kohl Stiftung, OneMind, NeuroTrauma Sciences,
Integra Neurosciences, European Research Council Horizon 2020
requires expert clinical interpretation and does not provide detailed quantitative outputs, which may have prognostic
importance. We aimed to use deep learning to reliably and efficiently quantify and detect different lesion types.
Methods Patients were recruited between Dec 9, 2014, and Dec 17, 2017, in 60 centres across Europe. We trained and
validated an initial convolutional neural network (CNN) on expert manual segmentations (dataset 1). This CNN was
used to automatically segment a new dataset of scans, which we then corrected manually (dataset 2). From this
dataset, we used a subset of scans to train a final CNN for multiclass, voxel-wise segmentation of lesion types. The
performance of this CNN was evaluated on a test subset. Performance was measured for lesion volume quantification,
lesion progression, and lesion detection and lesion volume classification. For lesion detection, external validation was
done on an independent set of 500 patients from India.
Findings 98 scans from one centre were included in dataset 1. Dataset 2 comprised 839 scans from 38 centres:
184 scans were used in the training subset and 655 in the test subset. Compared with manual reference, CNN-derived
lesion volumes showed a mean difference of 0·86 mL (95% CI –5·23 to 6·94) for intraparenchymal haemorrhage,
1·83 mL (–12·01 to 15·66) for extra-axial haemorrhage, 2·09 mL (–9·38 to 13·56) for perilesional oedema, and
0·07 mL (–1·00 to 1·13) for intraventricular haemorrhage.
Interpretation We show the ability of a CNN to separately segment, quantify, and detect multiclass haemorrhagic
lesions and perilesional oedema. These volumetric lesion estimates allow clinically relevant quantification of
lesion burden and progression, with potential applications for personalised treatment strategies and clinical research
in TBI.
Funding European Union 7th Framework Programme, Hannelore Kohl Stiftung, OneMind, NeuroTrauma Sciences,
Integra Neurosciences, European Research Council Horizon 2020
Date Issued
2020-06-01
Date Acceptance
2020-04-09
Citation
The Lancet. Digital Health, 2020, 2 (6), pp.e314-e322
ISSN
2589-7500
Publisher
Elsevier
Start Page
e314
End Page
e322
Journal / Book Title
The Lancet. Digital Health
Volume
2
Issue
6
Copyright Statement
© 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0
license (https://creativecommons.org/licenses/by/4.0/).
license (https://creativecommons.org/licenses/by/4.0/).
Sponsor
Engineering & Physical Science Research Council (E
Commission of the European Communities
Commission of the European Communities
Grant Number
EP/R511547/1
H2020 - 757173
HEALTH-F2-2013-602150
Subjects
Science & Technology
Life Sciences & Biomedicine
Medical Informatics
Medicine, General & Internal
General & Internal Medicine
CENTER-TBI
MRC CRASH
HEMORRHAGE
PROGRESSION
OUTCOMES
ADULTS
TRIAL
CARE
Publication Status
Published
Date Publish Online
2020-05-14