TY - GEN

T1 - Brain Tumor Segmentation with Self-supervised Enhance Region Post-processing

AU - Pnev, Sergey

AU - Groza, Vladimir

AU - Tuchinov, Bair

AU - Amelina, Evgeniya

AU - Pavlovskiy, Evgeniy

AU - Tolstokulakov, Nikolay

AU - Amelin, Mihail

AU - Golushko, Sergey

AU - Letyagin, Andrey

N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - In this paper, we extend the previous research works on the robust multi-sequences segmentation methods which allows to consider all available information from MRI scans by the composition of T1, T1C, T2 and T2-FLAIR sequences. It is based on the clinical radiology hypothesis and presents an efficient approach to combining and matching 3D methods to search for areas of comprised the GD-enhancing tumor in order to significantly improve the model’s performance of the particular applied numerical problem of brain tumor segmentation. Proposed in this paper method also demonstrates strong improvement on the segmentation problem. This conclusion was done with respect to Dice and Hausdorff metric, Sensitivity and Specificity compare to identical training/test procedure based only on any single sequence and regardless of the chosen neural network architecture. We achieved on the test set of 0.866, 0.921 and 0.869 for ET, WT, and TC Dice scores. Obtained results demonstrate significant performance improvement while combining several 3D approaches for considered tasks of brain tumor segmentation. In this work we provide the comparison of various 3D and 2D approaches, pre-processing to self-supervised clean data, post-processing optimization methods and the different backbone architectures.

AB - In this paper, we extend the previous research works on the robust multi-sequences segmentation methods which allows to consider all available information from MRI scans by the composition of T1, T1C, T2 and T2-FLAIR sequences. It is based on the clinical radiology hypothesis and presents an efficient approach to combining and matching 3D methods to search for areas of comprised the GD-enhancing tumor in order to significantly improve the model’s performance of the particular applied numerical problem of brain tumor segmentation. Proposed in this paper method also demonstrates strong improvement on the segmentation problem. This conclusion was done with respect to Dice and Hausdorff metric, Sensitivity and Specificity compare to identical training/test procedure based only on any single sequence and regardless of the chosen neural network architecture. We achieved on the test set of 0.866, 0.921 and 0.869 for ET, WT, and TC Dice scores. Obtained results demonstrate significant performance improvement while combining several 3D approaches for considered tasks of brain tumor segmentation. In this work we provide the comparison of various 3D and 2D approaches, pre-processing to self-supervised clean data, post-processing optimization methods and the different backbone architectures.

KW - Brain

KW - Deep learning

KW - Medical imaging

KW - MRI

KW - Neural network

KW - Segmentation

UR - http://www.scopus.com/inward/record.url?scp=85135189091&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/5a2996c0-6a6c-3631-95d0-84c46a597aa3/

U2 - 10.1007/978-3-031-09002-8_24

DO - 10.1007/978-3-031-09002-8_24

M3 - Conference contribution

AN - SCOPUS:85135189091

SN - 9783031090011

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

SP - 267

EP - 275

BT - Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers

A2 - Crimi, Alessandro

A2 - Bakas, Spyridon

PB - Springer Science and Business Media Deutschland GmbH

T2 - 7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021

Y2 - 27 September 2021 through 27 September 2021

ER -