Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

• Autorzy: Savareh B. A. , Emami H. , Hajiabadi M. , Ghafoori M. , Azimi S. M.

Identyfikatory

DOI

10.2478/pjmpe-2018-0007

• Języki publikacji: EN

Abstrakty

EN

Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation. This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation. Based on our findings, details about related studies including the datasets used, evaluation parameters, preferred architectures and complementary steps analyzed. Deep learning as a revolutionary idea in image processing, achieved brilliant results in brain tumor segmentation too. This can be continuing until the next revolutionary idea emerging.

Słowa kluczowe

EN

convolution neural network; brain tumor; segmentation; deep learning

• Wydawca: Polskie Towarzystwo Fizyki Medycznej ; De Gruyter
• Czasopismo: Polish Journal of Medical Physics and Engineering
• Rocznik: 2018
• Tom: Vol. 24, Iss. 1
• Strony: 48--53
• Opis fizyczny: Bibliogr. 111 poz., rys., tab.

Twórcy

autor

Savareh B. A.

• School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

autor

Emami H.

• Health Information Technology and Management department, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

autor

Hajiabadi M.

• Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
• Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran

autor

Ghafoori M.

• Department of Radiology, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

autor

Azimi S. M.

• Chair of Remote Sensing Technology, Technical University of Munich, Germany

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