Fully Automated Volumetric Measurement of Malignant Pleural Mesothelioma from Computed Tomography Images by Deep Learning: Preliminary Results of an Internal Validation

Owen Anderson, Owen Anderson, Andrew C. Kidd, Keith A. Goatman, Alexander J. Weir, Jeremy Voisey, Vismantas Dilys, Jan P. Siebert, Kevin G. Blyth, Kevin G. Blyth

2020

Abstract

Malignant Pleural Mesothelioma (MPM) is a cancer associated with prior exposure to asbestos fibres. Unlike most tumours, which are roughly spherical, MPM grows like a rind surrounding the lung. This irregular shape poses significant clinical and technical challenges. Accurate tumour measurements are necessary to determine treatment efficacy, but manual segmentation is tedious, time-consuming and associated with high intra- and inter-observer variation. In addition, uncertainty is compounded by poor differentiation in the computed tomography (CT) image between MPM and other common features. We describe herein an internal validation of a fully automatic tool to generate volumetric segmentations of MPM tumours using a convolutional neural network (CNN). The system was trained using the first 123 CT volumetric datasets from a planned total of 403 scans. Each scan was manually segmented to provide the expert ground truth. Evaluation was by seven-fold cross validation on a subset of 80/123 datasets that have full volumetric segmentations. The mean volume of MPM tumour in these datasets is 405.1 cm3 (standard deviation 271.5 cm3). Following three-dimensional binary closing of the manual annotations to improve inter-slice consistency, the mean volume difference between the manual and automatic measurements is 27.2 cm3, which is not significantly different from zero difference (p = 0:225). The 95% limits of agreement between the manual and automated measurements are between -417 and +363 cm3. The mean Dice overlap coefficient was 0.64, which is comparable with inter-observer measurements reported elsewhere. To our knowledge, this is the first algorithm of its kind that fully automates and evaluates measurement of the MPM tumour volume. The next step will be to evaluate the method on the remaining unseen multi-centre evaluation set. Such an algorithm has possible future application to pharmaceutical trials (where it offers a repeatable study end point) and to routine care (where it allows tumour progression to be assessed rapidly to enhance therapeutic clinical decision making).

Paper Citation

in Harvard Style

Anderson O., Kidd A., Goatman K., Weir A., Voisey J., Dilys V., P. Siebert J. and Blyth K. (2020). Fully Automated Volumetric Measurement of Malignant Pleural Mesothelioma from Computed Tomography Images by Deep Learning: Preliminary Results of an Internal Validation. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 2: BIOIMAGING; ISBN 978-989-758-398-8, SciTePress, pages 64-73. DOI: 10.5220/0008976100640073

in Bibtex Style

@conference{bioimaging20,
author={Owen Anderson and Andrew C. Kidd and Keith A. Goatman and Alexander J. Weir and Jeremy Voisey and Vismantas Dilys and Jan P. P. Siebert and Kevin G. Blyth},
title={Fully Automated Volumetric Measurement of Malignant Pleural Mesothelioma from Computed Tomography Images by Deep Learning: Preliminary Results of an Internal Validation},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 2: BIOIMAGING},
year={2020},
pages={64-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008976100640073},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 2: BIOIMAGING
TI - Fully Automated Volumetric Measurement of Malignant Pleural Mesothelioma from Computed Tomography Images by Deep Learning: Preliminary Results of an Internal Validation
SN - 978-989-758-398-8
AU - Anderson O.
AU - Kidd A.
AU - Goatman K.
AU - Weir A.
AU - Voisey J.
AU - Dilys V.
AU - P. Siebert J.
AU - Blyth K.
PY - 2020
SP - 64
EP - 73
DO - 10.5220/0008976100640073
PB - SciTePress