Evaluation of automated and semi-automated skull-stripping algorithms using similarity index and segmentation error

The skull-stripping in the MR brain image appears to be a key issue in neuroimage analysis. In this paper, we evaluated the accuracy and efficiency of both automated and semi-automated skull-stripping methods. The evaluation was performed on both simulated and real data with the ground truth in skul...

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Vydané v:Computers in biology and medicine Ročník 33; číslo 6; s. 495 - 507
Hlavní autori: Lee, Jong-Min, Yoon, Uicheul, Nam, Sang Hee, Kim, Jung-Hyun, Kim, In-Young, Kim, Sun I.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.11.2003
New York, NY Elsevier Science
Elsevier Limited
Predmet:
Algorithms
Biological and medical sciences
Brain - anatomy & histology
False negative rate
False positive rate
Humans
Image Processing, Computer-Assisted
Inter-rater reliability
Magnetic Resonance Imaging - methods
Medical sciences
Reproducibility of Results
Similarity index
Simulated T1-weighted MRI
Skull-stripping
False positive rate
Inter-rater reliability
Similarity index
Skull-stripping
False negative rate
Simulated T1-weighted MRI
ISSN:0010-4825, 1879-0534
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Abstract The skull-stripping in the MR brain image appears to be a key issue in neuroimage analysis. In this paper, we evaluated the accuracy and efficiency of both automated and semi-automated skull-stripping methods. The evaluation was performed on both simulated and real data with the ground truth in skull-stripping. Although automated method showed better efficient results, it should require additional intervention. In contrast to that, semi-automated method showed better accurate results, but it was time consuming and prone to operator bias. Therefore, it might be practical that the semi-automated method was used as the post-processing of the automated one.
AbstractList The skull-stripping in the MR brain image appears to be a key issue in neuroimage analysis. In this paper, we evaluated the accuracy and efficiency of both automated and semi-automated skull-stripping methods. The evaluation was performed on both simulated and real data with the ground truth in skull-stripping. Although automated method showed better efficient results, it should require additional intervention. In contrast to that, semi-automated method showed better accurate results, but it was time consuming and prone to operator bias. Therefore, it might be practical that the semi-automated method was used as the post-processing of the automated one.
The skull-stripping in the MR brain image appears to be a key issue in neuroimage analysis. In this paper, we evaluated the accuracy and efficiency of both automated and semi-automated skull-stripping methods. The evaluation was performed on both simulated and real data with the ground truth in skull-stripping. Although automated method showed better efficient results, it should require additional intervention. In contrast to that, semi-automated method showed better accurate results, but it was time consuming and prone to operator bias. Therefore, it might be practical that the semi-automated method was used as the post-processing of the automated one.The skull-stripping in the MR brain image appears to be a key issue in neuroimage analysis. In this paper, we evaluated the accuracy and efficiency of both automated and semi-automated skull-stripping methods. The evaluation was performed on both simulated and real data with the ground truth in skull-stripping. Although automated method showed better efficient results, it should require additional intervention. In contrast to that, semi-automated method showed better accurate results, but it was time consuming and prone to operator bias. Therefore, it might be practical that the semi-automated method was used as the post-processing of the automated one.
Author Nam, Sang Hee
Yoon, Uicheul
Kim, Jung-Hyun
Lee, Jong-Min
Kim, In-Young
Kim, Sun I.
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  surname: Lee
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Keywords False positive rate
Inter-rater reliability
Similarity index
Skull-stripping
False negative rate
Simulated T1-weighted MRI
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Snippet The skull-stripping in the MR brain image appears to be a key issue in neuroimage analysis. In this paper, we evaluated the accuracy and efficiency of both...
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SubjectTerms Algorithms
Biological and medical sciences
Brain - anatomy & histology
False negative rate
False positive rate
Humans
Image Processing, Computer-Assisted
Inter-rater reliability
Magnetic Resonance Imaging - methods
Medical sciences
Reproducibility of Results
Similarity index
Simulated T1-weighted MRI
Skull-stripping
Title Evaluation of automated and semi-automated skull-stripping algorithms using similarity index and segmentation error
URI https://www.clinicalkey.com/#!/content/1-s2.0-S0010482503000222
https://dx.doi.org/10.1016/S0010-4825(03)00022-2
https://www.ncbi.nlm.nih.gov/pubmed/12878233
https://www.proquest.com/docview/1030093274
https://www.proquest.com/docview/73506955
Volume 33
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