지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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Brain tumor surgery may be difficult, but it is also incredibly important. The technological improvements for traditional brain tumor surgeries have always been a focus to improve the precision of surgery and release the potential of the technology in this important area of the body. The need for precision during brain tumor surgery has led to an increase in Robotic-assisted surgeries (RAS). One of the challenges to the widespread acceptance of RAS in the neurosurgery is to recognize invisible tumor accurately. Therefore, it is important to detect brain tumor size and location because surgeon tries to remove as much tumor as possible. In this paper, we proposed brain tumor detection procedures for MRI (Magnetic Resonance Imaging) system. A method of automatic brain tumor detection is needed to accurately target the location of the lesion during brain tumor surgery and to report the location and size of the lesion. In the qualitative assessment, the proposed method showed better results than those obtained with other brain tumor detection methods. Comparisons among all assessment criteria indicated that the proposed method was significantly superior to the threshold method with respect to all assessment criteria. The proposed method was effective for detecting brain tumor.
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참고문헌 (26)
참고문헌 신청
A. R. Asthagiri / 2017 / Advances in brain tumor surgery / Neurol Clin 25:975~1003
J. A. Smith / 2016 / 30 years of neurosurgical robots : review and trends for manipulators and associated navigational systems / Annals of Biomedical Engineering 44(4):836~846
G. J. S. Litjens / 2017 / A survey on deep learning in medical image analysis / Medical Image Analysis 42:60~88
S. Hussain / 2018 / Segmentation of glioma tumors in brain using deep convolutional neural network / Neurocomputing 282:248~261
X. Zhao / 2018 / A deep learning model integrating FCNNs and CRFs for brain tumor segmentation / Medical Image Analysis 43:98~111
J. A. Smith / 2016 / 30 years of neurosurgical robots : review and trends for manipulators and associated navigational systems / Annals of Biomedical Engineering 44(4):836~846
G. J. S. Litjens / 2017 / A survey on deep learning in medical image analysis / Medical Image Analysis 42:60~88
S. Hussain / 2018 / Segmentation of glioma tumors in brain using deep convolutional neural network / Neurocomputing 282:248~261
X. Zhao / 2018 / A deep learning model integrating FCNNs and CRFs for brain tumor segmentation / Medical Image Analysis 43:98~111
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