TY - GEN
T1 - Tbi lesion segmentation in head CT
T2 - 5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019
AU - Monteiro, Miguel
AU - Kamnitsas, Konstantinos
AU - Ferrante, Enzo
AU - Mathieu, Francois
AU - McDonagh, Steven
AU - Cook, Sam
AU - Stevenson, Susan
AU - Das, Tilak
AU - Khetani, Aneesh
AU - Newman, Tom
AU - Zeiler, Fred
AU - Digby, Richard
AU - Coles, Jonathan P.
AU - Rueckert, Daniel
AU - Menon, David K.
AU - Newcombe, Virginia F.J.
AU - Glocker, Ben
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85085514537&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-46640-4_2
DO - 10.1007/978-3-030-46640-4_2
M3 - Conference contribution
AN - SCOPUS:85085514537
SN - 9783030466398
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 13
EP - 22
BT - Brainlesion
A2 - Crimi, Alessandro
A2 - Bakas, Spyridon
PB - Springer
Y2 - 17 October 2019 through 17 October 2019
ER -