Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Data Preprocessing Via Compositions Multi-Channel MRI Images to Improve Brain Tumor Segmentation. / Tolstokulakov, N.; Pavlovskiy, E.; Tuchinov, B. и др.
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).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
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