TY - GEN
T1 - Volumetric Analysis of Brain Tumor Magnetic Resonance Image
AU - Agustin, Hapsari Peni
AU - Hidayati, Hanik Badriyah
AU - Sooai, Adri Gabriel
AU - Ketut Eddy Purnama, I.
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Volumetric analysis of brain tumors is a decisive thing in the detection of brain tumors to determine the patient's lifetime followed by action to the patient. A few studies had been shown explicitly quantified the brain tumor volume while the analysis of brain tumor volumetric by expert limited with the huge data of brain tumor patient MRI. Thorough the importance of brain tumor analysis in clinical used, the purpose of this research is to evaluate the similarity of a semi-automatic segmentation tool for brain tumor image analysis. The agreement was compared by using differences of means with 95% limits of agreement (LoA). Brain tumor segmentation was obtained by using Fast Marching and Grow Cut segmentation methods. Preoperative MRI images of 20 T2 MRI of low-grade glioma patients from The Cancer Imaging Archive (TCIA) database were used to analyze brain tumor volume. The volume obtained from the two segmentation methods is based on the similarity between the two using the intra-method agreement between two segmentation methods with a 95% limit of agreement (LoA) value and difference volume average of 920 mm3 or 0.92 mL. Its shown that both methods had the same performance.
AB - Volumetric analysis of brain tumors is a decisive thing in the detection of brain tumors to determine the patient's lifetime followed by action to the patient. A few studies had been shown explicitly quantified the brain tumor volume while the analysis of brain tumor volumetric by expert limited with the huge data of brain tumor patient MRI. Thorough the importance of brain tumor analysis in clinical used, the purpose of this research is to evaluate the similarity of a semi-automatic segmentation tool for brain tumor image analysis. The agreement was compared by using differences of means with 95% limits of agreement (LoA). Brain tumor segmentation was obtained by using Fast Marching and Grow Cut segmentation methods. Preoperative MRI images of 20 T2 MRI of low-grade glioma patients from The Cancer Imaging Archive (TCIA) database were used to analyze brain tumor volume. The volume obtained from the two segmentation methods is based on the similarity between the two using the intra-method agreement between two segmentation methods with a 95% limit of agreement (LoA) value and difference volume average of 920 mm3 or 0.92 mL. Its shown that both methods had the same performance.
KW - Difference Volume Average
KW - Fast Marching
KW - Grow Cut
KW - Limits of Agreement
UR - http://www.scopus.com/inward/record.url?scp=85084441557&partnerID=8YFLogxK
U2 - 10.1109/CENIM48368.2019.8973300
DO - 10.1109/CENIM48368.2019.8973300
M3 - Conference contribution
AN - SCOPUS:85084441557
T3 - 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding
BT - 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019
Y2 - 19 November 2019 through 20 November 2019
ER -
