Automatic segmentation of brain tumor in multi-contrast magnetic resonance using deep neural network

Eduardo Cavieres; Cristian Tejos; Rodrigo Salas; Julio Sotelo

doi:10.1117/12.2670375

6 March 2023 Automatic segmentation of brain tumor in multi-contrast magnetic resonance using deep neural network

Eduardo Cavieres, Cristian Tejos, Rodrigo Salas, Julio Sotelo

Proceedings Volume 12567, 18th International Symposium on Medical Information Processing and Analysis; 125670B (2023) https://doi.org/10.1117/12.2670375
Event: 18th International Symposium on Medical Information Processing and Analysis, 2022, Valparaíso, Chile

Abstract

Among all the tumors that can affect the brain, gliomas are the most frequent, thus is important to get a correct characterization and delimitation of this malformation to provide the best diagnosis and treatment possible. Nevertheless, there are some issues when dealing with segmenting tumors, it can be a long and tedious labor, which makes it prone to mistakes. To solve those problems, several techniques were proposed, including automatic and semiautomatic segmentation. In this work, we propose the use of a U-net architecture-based deep neural network to automatically realize segmentations of tumors on magnetic resonance brain images obtained from the BRATS 2020 database, which provides T1, T1 contrast enhancement (T1ce), T2, and FLAIR images for each subject. The database has a total of 1476 images distributed in 369 patients, that were shuffled into the training set with 70% of the subjects, and the test set with a percentage of 30%. Our results got a 91.6% DICE value for the validation, from a 91.6% for necrotic core (NET), 91.7% for peritumoral edema (PE), and a 91.4% for enhancing tumor (ET). After the training, we got 55.5%,66.5%, and 68.6% DICE values for NET, PE and ET respectively. We also calculated the whole tumor (WT) segmentation performance, reaching a 78.8% precision and the tumor core (TC) segmentation which reach 75,5% precision.

Citation Download Citation

Eduardo Cavieres, Cristian Tejos, Rodrigo Salas, and Julio Sotelo "Automatic segmentation of brain tumor in multi-contrast magnetic resonance using deep neural network", Proc. SPIE 12567, 18th International Symposium on Medical Information Processing and Analysis, 125670B (6 March 2023); https://doi.org/10.1117/12.2670375

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Tumors

Image segmentation

Brain

Education and training

Databases

Neuroimaging

Magnetic resonance imaging

Show All Keywords

Keywords/Phrases

Search In:

Publication Years