Volume 80, Issue 4 (July 2022)
Tehran Univ Med J 2022, 80(4): 293-299 |
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Papi Z, Abedi I, Dalvand F, Amouheidari A. Automatic segmentation of glioma tumors from BraTS 2018 challenge dataset using a 2D U-Net network. Tehran Univ Med J 2022; 80 (4) :293-299
URL: http://tumj.tums.ac.ir/article-1-11811-en.html
URL: http://tumj.tums.ac.ir/article-1-11811-en.html
1- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
2- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. ,i.abedi@med.mui.ac.ir
3- Department of Radiation Engineering, School of Medicine, Shahid Beheshti University, Tehran, Iran.
4- Department of Radiotherapy, Isfahan Milad Hospital, Isfahan, Iran.
2- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. ,
3- Department of Radiation Engineering, School of Medicine, Shahid Beheshti University, Tehran, Iran.
4- Department of Radiotherapy, Isfahan Milad Hospital, Isfahan, Iran.
Abstract: (1129 Views)
Background: Glioma is the most common primary brain tumor, and early detection of tumors is important in the treatment planning for the patient. The precise segmentation of the tumor and intratumoral areas on the MRI by a radiologist is the first step in the diagnosis, which, in addition to the consuming time, can also receive different diagnoses from different physicians. The aim of this study was to provide an automated method for segmenting the tumor and intratumoral areas.
Methods: This is a fundamental-applied study that was conducted from May 2020 to September 2021 using multimodal MRI images of 285 patients with glioma tumors from the BraTS 2018 Database. This database was collected from 19 different MRI imaging centers, including multimodal MRI images of 210 HGG patients, and 75 LGG patients. In this study, a 2D U-Net architecture was designed with a patch-based method for training, which comprises an encoding path for feature extraction and a symmetrical decoding path. The training of this network was performed in three separate stages, using data from high-grade gliomas (HGG), and low-grade gliomas (LGG), and combining two groups of 210, 75, and 220 patients, respectively.
Methods: This is a fundamental-applied study that was conducted from May 2020 to September 2021 using multimodal MRI images of 285 patients with glioma tumors from the BraTS 2018 Database. This database was collected from 19 different MRI imaging centers, including multimodal MRI images of 210 HGG patients, and 75 LGG patients. In this study, a 2D U-Net architecture was designed with a patch-based method for training, which comprises an encoding path for feature extraction and a symmetrical decoding path. The training of this network was performed in three separate stages, using data from high-grade gliomas (HGG), and low-grade gliomas (LGG), and combining two groups of 210, 75, and 220 patients, respectively.
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Results: The proposed model estimated the Dice Similarity Coefficient (DSC) results in HGG datasets 0.85, 0.85, 0.77, LGG datasets 0.80, 0.66, 0.51, and the combination of the two groups 0.88, 0.79, 0.77 for regions the whole tumor, tumor core, and enhancing region in the training dataset, respectively. The results related to Hussdorf Distance (HD) for HGG datasets were 8.24, 9.92, 4.43, LGG datasets 11.5, 11.31, 2.23, and the combination of the two groups 7.20, 8.82, 4.43 for regions the whole tumor, tumor core, and enhancing region in the training dataset, respectively.
Conclusion: Using the U-Net network can help physicians in the accurate segmentation of the tumor and its various areas, as well as increase the survival rate of these patients and improve their quality of life through accurate diagnosis and early treatment. |
Type of Study: Original Article |
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