TY - GEN

T1 - Data Preprocessing via Multi-sequences MRI Mixture to Improve Brain Tumor Segmentation

AU - Groza, Vladimir

AU - Tuchinov, Bair

AU - Pavlovskiy, Evgeniy

AU - Amelina, Evgeniya

AU - Amelin, Mihail

AU - Golushko, Sergey

AU - Letyagin, Andrey

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Automatic brain tumor segmentation is one of the crucial problems nowadays among other directions and domains where daily clinical workflow requires to put a lot of efforts while studying computer tomography (CT) or structural magnetic resonance imaging (MRI) scans of patients with various pathologies. The MRI is the most common method of primary detection, non-invasive diagnostics and a source of recommendations for further treatment. The brain is a complex structure, different areas of which have different functional significance. In this paper, we propose a robust pre-processing technique which allows to consider all available information from MRI scans by composition of T1, T1C and FLAIR sequences in the unique input. Such approach enriches the input data for the automatic segmentation process and helps to improve the accuracy of the segmentation performance. Proposed method demonstrates significant improvement on the binary segmentation problem with respect to Dice and Recall metrics compare to similar training/evaluation procedure based on any single sequence regardless of the chosen neural network architecture. Obtained results demonstrates significant evaluation improvement while combining three MRI sequences either as weighted mixture to get 1-channel mixed up image or in the 3-channel RGB like image for both considered problems - binary brain tumor segmentation with and without inclusion of edema in the region of interest (ROI). Final improvements on the test part of data set are in the range of 5.6–9.1% on the single-fold trained model according to the Dice metric with the best value of 0.902 without considering a priori “empty” slides. We also demonstrate strong impact on the Recall metric with the growth up to 9.5%. Additionally this approach demonstrates significant improvement according to the Recall metric getting the increase by up to 11%.

AB - Automatic brain tumor segmentation is one of the crucial problems nowadays among other directions and domains where daily clinical workflow requires to put a lot of efforts while studying computer tomography (CT) or structural magnetic resonance imaging (MRI) scans of patients with various pathologies. The MRI is the most common method of primary detection, non-invasive diagnostics and a source of recommendations for further treatment. The brain is a complex structure, different areas of which have different functional significance. In this paper, we propose a robust pre-processing technique which allows to consider all available information from MRI scans by composition of T1, T1C and FLAIR sequences in the unique input. Such approach enriches the input data for the automatic segmentation process and helps to improve the accuracy of the segmentation performance. Proposed method demonstrates significant improvement on the binary segmentation problem with respect to Dice and Recall metrics compare to similar training/evaluation procedure based on any single sequence regardless of the chosen neural network architecture. Obtained results demonstrates significant evaluation improvement while combining three MRI sequences either as weighted mixture to get 1-channel mixed up image or in the 3-channel RGB like image for both considered problems - binary brain tumor segmentation with and without inclusion of edema in the region of interest (ROI). Final improvements on the test part of data set are in the range of 5.6–9.1% on the single-fold trained model according to the Dice metric with the best value of 0.902 without considering a priori “empty” slides. We also demonstrate strong impact on the Recall metric with the growth up to 9.5%. Additionally this approach demonstrates significant improvement according to the Recall metric getting the increase by up to 11%.

KW - Brain

KW - Deep learning

KW - Medical imaging

KW - MRI

KW - Neural network

KW - Segmentation

UR - http://www.scopus.com/inward/record.url?scp=85085177735&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-45385-5_62

DO - 10.1007/978-3-030-45385-5_62

M3 - Conference contribution

AN - SCOPUS:85085177735

SN - 9783030453848

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 695

EP - 704

BT - Bioinformatics and Biomedical Engineering - 8th International Work-Conference, IWBBIO 2020, Proceedings

A2 - Rojas, Ignacio

A2 - Valenzuela, Olga

A2 - Rojas, Fernando

A2 - Herrera, Luis Javier

A2 - Ortuño, Francisco

PB - Springer Gabler

T2 - 8th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2020

Y2 - 6 May 2020 through 8 May 2020

ER -