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Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation. / Tolstokulakov, N.; Pavlovskiy, E.; Tuchinov, B. et al.

ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9153416 (ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Tolstokulakov, N, Pavlovskiy, E, Tuchinov, B, Amelina, E, Amelin, M, Letyagin, A, Golushko, S & Groza, V 2020, Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation. in ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings., 9153416, ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings, Institute of Electrical and Electronics Engineers Inc., 17th IEEE International Symposium on Biomedical Imaging Workshops, ISBI Workshops 2020, Iowa City, United States, 04.04.2020. https://doi.org/10.1109/ISBIWorkshops50223.2020.9153416

APA

Tolstokulakov, N., Pavlovskiy, E., Tuchinov, B., Amelina, E., Amelin, M., Letyagin, A., Golushko, S., & Groza, V. (2020). Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation. In ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings [9153416] (ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISBIWorkshops50223.2020.9153416

Vancouver

Tolstokulakov N, Pavlovskiy E, Tuchinov B, Amelina E, Amelin M, Letyagin A et al. Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation. In ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9153416. (ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings). doi: 10.1109/ISBIWorkshops50223.2020.9153416

Author

Tolstokulakov, N. ; Pavlovskiy, E. ; Tuchinov, B. et al. / Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation. ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings).

BibTeX

@inproceedings{e2cce710c58a44a3a154d624aec1ef19,
title = "Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation",
abstract = "Magnetic resonance imaging (MRI) stays one of the most essential noninvasive methods for brain diagnostics. It allows obtaining the detailed 3D image of the brain, including various types of soft tissues. In this paper, we compare the influence of the multichannel data composition approach on the model's performance. We consider the binary brain tumor segmentation problem evaluating the Dice, Recall and Precision metrics. One common way to process the medical images with the use of neural networks is to use 2D slices as the input. In contrast to the RGB images, there are plenty of methods of how to combine the multi-channel MRI data structure into the common format for ML-based algorithms. After evaluating several possible combinations we demonstrate the most performance improvement by 6-7% in Dice Recall metrics using the pseudo-RGB approach.",
keywords = "Brain Tumor, Deep Learning, Multi-channel MRI, Neural Network, Segmentation",
author = "N. Tolstokulakov and E. Pavlovskiy and B. Tuchinov and E. Amelina and M. Amelin and A. Letyagin and S. Golushko and V. Groza",
note = "N. Tolstokulakov et al., {"}Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation,{"} 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops), Iowa City, IA, USA, 2020, pp. 1-4, doi: 10.1109/ISBIWorkshops50223.2020.9153416; 17th IEEE International Symposium on Biomedical Imaging Workshops, ISBI Workshops 2020 ; Conference date: 04-04-2020",
year = "2020",
month = apr,
day = "1",
doi = "10.1109/ISBIWorkshops50223.2020.9153416",
language = "English",
isbn = "978-1-7281-7402-0",
series = "ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings",
address = "United States",

}

RIS

TY - GEN

T1 - Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation

AU - Tolstokulakov, N.

AU - Pavlovskiy, E.

AU - Tuchinov, B.

AU - Amelina, E.

AU - Amelin, M.

AU - Letyagin, A.

AU - Golushko, S.

AU - Groza, V.

N1 - N. Tolstokulakov et al., "Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation," 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops), Iowa City, IA, USA, 2020, pp. 1-4, doi: 10.1109/ISBIWorkshops50223.2020.9153416

PY - 2020/4/1

Y1 - 2020/4/1

N2 - Magnetic resonance imaging (MRI) stays one of the most essential noninvasive methods for brain diagnostics. It allows obtaining the detailed 3D image of the brain, including various types of soft tissues. In this paper, we compare the influence of the multichannel data composition approach on the model's performance. We consider the binary brain tumor segmentation problem evaluating the Dice, Recall and Precision metrics. One common way to process the medical images with the use of neural networks is to use 2D slices as the input. In contrast to the RGB images, there are plenty of methods of how to combine the multi-channel MRI data structure into the common format for ML-based algorithms. After evaluating several possible combinations we demonstrate the most performance improvement by 6-7% in Dice Recall metrics using the pseudo-RGB approach.

AB - Magnetic resonance imaging (MRI) stays one of the most essential noninvasive methods for brain diagnostics. It allows obtaining the detailed 3D image of the brain, including various types of soft tissues. In this paper, we compare the influence of the multichannel data composition approach on the model's performance. We consider the binary brain tumor segmentation problem evaluating the Dice, Recall and Precision metrics. One common way to process the medical images with the use of neural networks is to use 2D slices as the input. In contrast to the RGB images, there are plenty of methods of how to combine the multi-channel MRI data structure into the common format for ML-based algorithms. After evaluating several possible combinations we demonstrate the most performance improvement by 6-7% in Dice Recall metrics using the pseudo-RGB approach.

KW - Brain Tumor

KW - Deep Learning

KW - Multi-channel MRI

KW - Neural Network

KW - Segmentation

UR - http://www.scopus.com/inward/record.url?scp=85090425870&partnerID=8YFLogxK

U2 - 10.1109/ISBIWorkshops50223.2020.9153416

DO - 10.1109/ISBIWorkshops50223.2020.9153416

M3 - Conference contribution

AN - SCOPUS:85090425870

SN - 978-1-7281-7402-0

T3 - ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings

BT - ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th IEEE International Symposium on Biomedical Imaging Workshops, ISBI Workshops 2020

Y2 - 4 April 2020

ER -

ID: 25309784