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.
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Notes
- 1.
For the experiments with \(1\times 1 \times 4\) resolution, we turn some of the isotropic kernels into anisotropic \(3\times 3 \times 1\) kernels in order to obtain approximately the same receptive field as in the experiments with isotropic resolution. This did not affect the performance.
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Acknowledgments
This work is partially funded by a European Union FP7 grant (CENTER-TBI; Agreement No: 60215) and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Agreement No: 757173, project MIRA, ERC-2017-STG). EF is supported by the AXA Research Fund and UNL (CAID-50220140100084LI). KK is supported by the President’s PhD Scholarship of Imperial College London. VFJN is supported by a Health Foundation/Academy of Medical Sciences Clinician Scientist Fellowship. DKM is supported by funding from the National Institute for Health Research (NIHR) through a Senior Investigator award and the Cambridge Biomedical Research Centre at the Cambridge University Hospitals National Health Service (NHS) Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care.
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Monteiro, M. et al. (2020). TBI Lesion Segmentation in Head CT: Impact of Preprocessing and Data Augmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11992. Springer, Cham. https://doi.org/10.1007/978-3-030-46640-4_2
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