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Tbi lesion segmentation in head CT: Impact of preprocessing and data augmentation

Monteiro M, Kamnitsas K, Ferrante E, Mathieu F, Mcdonagh S, Cook S, Stevenson S, Das T, Khetani A, Newman T, Zeiler F, Digby R, Coles JP, Rueckert D, Menon DK, Newcombe VFJ, Glocker B (2020)

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Publication Type: Conference contribution

Publication year: 2020

Journal

Publisher: Springer

Book Volume: 11992 LNCS

Pages Range: 13-22

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Shenzhen, CHN

ISBN: 9783030466398

DOI: 10.1007/978-3-030-46640-4_2

Abstract

Automatic segmentation of lesions in head CT provides key information for patient management, prognosis and disease monitoring. Despite its clinical importance, method development has mostly focused on multi-parametric MRI. Analysis of the brain in CT is challenging due to limited soft tissue contrast and its mono-modal nature. We study the under-explored problem of fine-grained CT segmentation of multiple lesion types (core, blood, oedema) in traumatic brain injury (TBI). We observe that preprocessing and data augmentation choices greatly impact the segmentation accuracy of a neural network, yet these factors are rarely thoroughly assessed in prior work. We design an empirical study that extensively evaluates the impact of different data preprocessing and augmentation methods. We show that these choices can have an impact of up to 18% DSC. We conclude that resampling to isotropic resolution yields improved performance, skull-stripping can be replaced by using the right intensity window, and affine-to-atlas registration is not necessary if we use sufficient spatial augmentation. Since both skull-stripping and affine-to-atlas registration are susceptible to failure, we recommend their alternatives to be used in practice. We believe this is the first work to report results for fine-grained multi-class segmentation of TBI in CT. Our findings may inform further research in this under-explored yet clinically important task of automatic head CT lesion segmentation.

Involved external institutions

How to cite

APA:

Monteiro, M., Kamnitsas, K., Ferrante, E., Mathieu, F., Mcdonagh, S., Cook, S.,... Glocker, B. (2020). Tbi lesion segmentation in head CT: Impact of preprocessing and data augmentation. In Alessandro Crimi, Spyridon Bakas (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 13-22). Shenzhen, CHN: Springer.

MLA:

Monteiro, Miguel, et al. "Tbi lesion segmentation in head CT: Impact of preprocessing and data augmentation." Proceedings of the 5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Alessandro Crimi, Spyridon Bakas, Springer, 2020. 13-22.

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