A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture
Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a treatment. Using 3D U-net architecture to perform se...
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| Published in: | BMC bioinformatics Vol. 23; no. 1; pp. 251 - 21 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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London
BioMed Central
24.06.2022
Springer Nature B.V BMC |
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| ISSN: | 1471-2105, 1471-2105 |
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| Abstract | Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a treatment. Using 3D U-net architecture to perform semantic segmentation on brain tumor dataset is at the core of deep learning. In this paper, we present a unique cloud-based 3D U-Net method to perform brain tumor segmentation using BRATS dataset. The system was effectively trained by using Adam optimization solver by utilizing multiple hyper parameters. We got an average dice score of 95% which makes our method the first cloud-based method to achieve maximum accuracy. The dice score is calculated by using Sørensen-Dice similarity coefficient. We also performed an extensive literature review of the brain tumor segmentation methods implemented in the last five years to get a state-of-the-art picture of well-known methodologies with a higher dice score. In comparison to the already implemented architectures, our method ranks on top in terms of accuracy in using a cloud-based 3D U-Net framework for glioma segmentation. |
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| AbstractList | Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a treatment. Using 3D U-net architecture to perform semantic segmentation on brain tumor dataset is at the core of deep learning. In this paper, we present a unique cloud-based 3D U-Net method to perform brain tumor segmentation using BRATS dataset. The system was effectively trained by using Adam optimization solver by utilizing multiple hyper parameters. We got an average dice score of 95% which makes our method the first cloud-based method to achieve maximum accuracy. The dice score is calculated by using Sørensen-Dice similarity coefficient. We also performed an extensive literature review of the brain tumor segmentation methods implemented in the last five years to get a state-of-the-art picture of well-known methodologies with a higher dice score. In comparison to the already implemented architectures, our method ranks on top in terms of accuracy in using a cloud-based 3D U-Net framework for glioma segmentation.Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a treatment. Using 3D U-net architecture to perform semantic segmentation on brain tumor dataset is at the core of deep learning. In this paper, we present a unique cloud-based 3D U-Net method to perform brain tumor segmentation using BRATS dataset. The system was effectively trained by using Adam optimization solver by utilizing multiple hyper parameters. We got an average dice score of 95% which makes our method the first cloud-based method to achieve maximum accuracy. The dice score is calculated by using Sørensen-Dice similarity coefficient. We also performed an extensive literature review of the brain tumor segmentation methods implemented in the last five years to get a state-of-the-art picture of well-known methodologies with a higher dice score. In comparison to the already implemented architectures, our method ranks on top in terms of accuracy in using a cloud-based 3D U-Net framework for glioma segmentation. Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a treatment. Using 3D U-net architecture to perform semantic segmentation on brain tumor dataset is at the core of deep learning. In this paper, we present a unique cloud-based 3D U-Net method to perform brain tumor segmentation using BRATS dataset. The system was effectively trained by using Adam optimization solver by utilizing multiple hyper parameters. We got an average dice score of 95% which makes our method the first cloud-based method to achieve maximum accuracy. The dice score is calculated by using Sørensen-Dice similarity coefficient. We also performed an extensive literature review of the brain tumor segmentation methods implemented in the last five years to get a state-of-the-art picture of well-known methodologies with a higher dice score. In comparison to the already implemented architectures, our method ranks on top in terms of accuracy in using a cloud-based 3D U-Net framework for glioma segmentation. Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a treatment. Using 3D U-net architecture to perform semantic segmentation on brain tumor dataset is at the core of deep learning. In this paper, we present a unique cloud-based 3D U-Net method to perform brain tumor segmentation using BRATS dataset. The system was effectively trained by using Adam optimization solver by utilizing multiple hyper parameters. We got an average dice score of 95% which makes our method the first cloud-based method to achieve maximum accuracy. The dice score is calculated by using Sørensen-Dice similarity coefficient. We also performed an extensive literature review of the brain tumor segmentation methods implemented in the last five years to get a state-of-the-art picture of well-known methodologies with a higher dice score. In comparison to the already implemented architectures, our method ranks on top in terms of accuracy in using a cloud-based 3D U-Net framework for glioma segmentation. Abstract Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a treatment. Using 3D U-net architecture to perform semantic segmentation on brain tumor dataset is at the core of deep learning. In this paper, we present a unique cloud-based 3D U-Net method to perform brain tumor segmentation using BRATS dataset. The system was effectively trained by using Adam optimization solver by utilizing multiple hyper parameters. We got an average dice score of 95% which makes our method the first cloud-based method to achieve maximum accuracy. The dice score is calculated by using Sørensen-Dice similarity coefficient. We also performed an extensive literature review of the brain tumor segmentation methods implemented in the last five years to get a state-of-the-art picture of well-known methodologies with a higher dice score. In comparison to the already implemented architectures, our method ranks on top in terms of accuracy in using a cloud-based 3D U-Net framework for glioma segmentation. |
| ArticleNumber | 251 |
| Author | Farooq, Qurat ul Ain Tu, Shanshan Shaukat, Zeeshan Xiao, Chuangbai Ali, Saqib |
| Author_xml | – sequence: 1 givenname: Zeeshan surname: Shaukat fullname: Shaukat, Zeeshan email: zee@emails.bjut.edu.cn organization: Faculty of Information Technology, Beijing University of Technology, Faculty of Computer Science, University of South Asia – sequence: 2 givenname: Qurat ul Ain surname: Farooq fullname: Farooq, Qurat ul Ain organization: Faculty of Environmental and Life Sciences, Beijing University of Technology – sequence: 3 givenname: Shanshan surname: Tu fullname: Tu, Shanshan organization: Faculty of Information Technology, Beijing University of Technology – sequence: 4 givenname: Chuangbai surname: Xiao fullname: Xiao, Chuangbai email: cbxiao@bjut.edu.cn organization: Faculty of Information Technology, Beijing University of Technology – sequence: 5 givenname: Saqib surname: Ali fullname: Ali, Saqib organization: Faculty of Information Technology, Beijing University of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35751030$$D View this record in MEDLINE/PubMed |
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| Keywords | Deep learning Brain tumor Cloud computing Semantic segmentation 3D U-Net |
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| Snippet | Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the... Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the... Abstract Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary... |
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| SubjectTerms | 3D U-Net Accuracy Algorithms Bioinformatics Biomedical and Life Sciences Brain Brain cancer Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology Brain tumor Brain tumors Cloud Computing Computational Biology/Bioinformatics Computer Appl. in Life Sciences Datasets Deep Learning Experiments Glioma Humans Image processing Image Processing, Computer-Assisted - methods Image segmentation Life Sciences Literature reviews Magnetic resonance imaging Magnetic Resonance Imaging - methods Mathematical analysis Medical diagnosis Methods Microarrays Neural networks Optimization Semantic segmentation Semantics Tomography Traumatic brain injury Tumors |
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| Title | A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture |
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