U-Net and Active Contour Methods for Brain Tumour Segmentation and Visualization

Gliomas are a type of brain and spinal tumour. They originate from glial cells that form the stroma of nerve tissue. Gliomas constitute about 70% of all intracranial tumours and are perceived as the leading cause of death due to brain tumours. This paper presents a comparison of three approaches for...

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Bibliographic Details
Published in2020 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 7
Main Authors Kot, Estera, Krawczyk, Zuzanna, Siwek, Krzysztof, Czwarnowski, Piotr S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2020
Subjects
Active Contour
Active contours
Bones
Brain Tumour
Computed tomography
Convolutional Neural Networks
Glioblastoma
Isotopes
PET
Phantoms
Tumors
U-Net
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Summary:Gliomas are a type of brain and spinal tumour. They originate from glial cells that form the stroma of nerve tissue. Gliomas constitute about 70% of all intracranial tumours and are perceived as the leading cause of death due to brain tumours. This paper presents a comparison of three approaches for glioma detection, volume computation and 3D visualization. The classic approaches based on thresholding and active contour methods were compared with a deep learning implementation. The state-of-the-art model, named U-Net, was used to segment biomedical images which effectively removed bone images from computed tomography (CT) scans. To portion the tumour area, the Morphological Geodesic Active Contour method was applied. Model training was enriched by implementing a data augmentation strategy. Numerical results of tumour volume in cm3 were presented as well as a 3D visualization example.
ISSN:2161-4407
DOI:10.1109/IJCNN48605.2020.9207572