TY - GEN

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

AU - Letyagin, Andrey

AU - Golushko, Sergey

AU - Amelin, Mikhail

AU - Tuchinov, Bair

AU - Amelina, Evgeniya

AU - Tolstokulakov, Nikolay

AU - Pavlovskiy, Evgeniy

AU - Groza, Vladimir

N1 - Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/7

Y1 - 2020/7

N2 - In this paper, we extend the previous work on the 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 also demonstrate significant improvement on the multi-class segmentation problem with respect to Dice metrics compare to similar training / evaluation procedure based on any single sequence regardless of the chosen neural network architecture. Obtained results demonstrate significant evaluation improvement while combining three MRI sequences in the 3-channel RGB like image for considered problem of multi-class brain tumor segmentation. We also provide results of comparison of various gradient descent optimization methods and of different backbone architectures. We found that different algorithms worked best for different tumors, but no single algorithm ranked in the top for all types of tumors simultaneously. Final improvements on the test part of our dataset are in the range of 6 - 9% on the trained model according to the Dice metric with the best value of 0.949.

AB - In this paper, we extend the previous work on the 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 also demonstrate significant improvement on the multi-class segmentation problem with respect to Dice metrics compare to similar training / evaluation procedure based on any single sequence regardless of the chosen neural network architecture. Obtained results demonstrate significant evaluation improvement while combining three MRI sequences in the 3-channel RGB like image for considered problem of multi-class brain tumor segmentation. We also provide results of comparison of various gradient descent optimization methods and of different backbone architectures. We found that different algorithms worked best for different tumors, but no single algorithm ranked in the top for all types of tumors simultaneously. Final improvements on the test part of our dataset are in the range of 6 - 9% on the trained model according to the Dice metric with the best value of 0.949.

KW - Deep Learning

KW - Medical Imaging

KW - Neural Network

KW - Semantic segmentation

UR - http://www.scopus.com/inward/record.url?scp=85094864553&partnerID=8YFLogxK

UR - https://www.elibrary.ru/item.asp?id=44042229

U2 - 10.1109/CSGB51356.2020.9214645

DO - 10.1109/CSGB51356.2020.9214645

M3 - Conference contribution

AN - SCOPUS:85094864553

T3 - Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

SP - 185

EP - 189

BT - Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

Y2 - 6 July 2020 through 10 July 2020

ER -