Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Multi-Class Brain Tumor Segmentation via 3d and 2d Neural Networks. / Pnev, Sergey; Groza, Vladimir; Tuchinov, Bair и др.
ISBI 2022 - Proceedings: 2022 IEEE International Symposium on Biomedical Imaging. IEEE Computer Society, 2022. (Proceedings - International Symposium on Biomedical Imaging; Том 2022-March).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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TY - GEN
T1 - Multi-Class Brain Tumor Segmentation via 3d and 2d Neural Networks
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 - Funding Information: The reported study was funded by RFBR according to the research project No 19-29-01103. Publisher Copyright: © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Brain tumor segmentation is an important and time-consuming part of the usual clinical diagnosis process. Multi-class segmentation of different tumor types is a challenging task, due to the differences in shape, size, location and scanner parameters. Many 2D and 3D convolution neural network architectures have been proposed to address this problem achieving a significant success. It is well known that 2D approach is generally faster and more popular in the most of such problems. However, the usage of 3D models allows us to simultaneously improve the quality of segmentation. Accounting the context along the sagittal plane leads to the learning of 3-dimensional features that we used for computationally expensive 3D operations what in its turn increases the learning time as well as decreases the speed of operation.In this paper, we compare the 2D and 3D approaches on 2 datasets with MRI images: the one from the BraTS 2020 competition and a private Siberian Brain tumor dataset. In each dataset, any single scan is represented by 4 sequences T1, T1C, T2 and T2-Flair, annotated by two certified neuro-radiologist specialists. The datasets differ from each other in the dimension, grade set and tumor type. Numerical comparison was performed based on the Dice score index. We provide the case by case analysis for the samples that caused most difficulties for the models. The results obtained in our work demonstrate the significant over performing of 3D methods keeping robustness in a regard of data source and type that allow us to get a little closer to AI-assisted diagnosis.
AB - Brain tumor segmentation is an important and time-consuming part of the usual clinical diagnosis process. Multi-class segmentation of different tumor types is a challenging task, due to the differences in shape, size, location and scanner parameters. Many 2D and 3D convolution neural network architectures have been proposed to address this problem achieving a significant success. It is well known that 2D approach is generally faster and more popular in the most of such problems. However, the usage of 3D models allows us to simultaneously improve the quality of segmentation. Accounting the context along the sagittal plane leads to the learning of 3-dimensional features that we used for computationally expensive 3D operations what in its turn increases the learning time as well as decreases the speed of operation.In this paper, we compare the 2D and 3D approaches on 2 datasets with MRI images: the one from the BraTS 2020 competition and a private Siberian Brain tumor dataset. In each dataset, any single scan is represented by 4 sequences T1, T1C, T2 and T2-Flair, annotated by two certified neuro-radiologist specialists. The datasets differ from each other in the dimension, grade set and tumor type. Numerical comparison was performed based on the Dice score index. We provide the case by case analysis for the samples that caused most difficulties for the models. The results obtained in our work demonstrate the significant over performing of 3D methods keeping robustness in a regard of data source and type that allow us to get a little closer to AI-assisted diagnosis.
KW - Brain
KW - Deep Learning
KW - Medical Imaging
KW - MRI
KW - Neural Network
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85129597274&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/aff9af67-94a5-36ac-b418-726fca5fd8ff/
U2 - 10.1109/ISBI52829.2022.9761538
DO - 10.1109/ISBI52829.2022.9761538
M3 - Conference contribution
AN - SCOPUS:85129597274
SN - 9781665429238
T3 - Proceedings - International Symposium on Biomedical Imaging
BT - ISBI 2022 - Proceedings
PB - IEEE Computer Society
T2 - 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022
Y2 - 28 March 2022 through 31 March 2022
ER -
ID: 36075783