Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imaging
DC Field | Value | Language |
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dc.contributor.author | Cho, Yongwon | - |
dc.contributor.author | Cho, Hyungjoon | - |
dc.contributor.author | Shim, Jaemin | - |
dc.contributor.author | Choi, Jong-Il | - |
dc.contributor.author | Kim, Young-Hoon | - |
dc.contributor.author | Kim, Namkug | - |
dc.contributor.author | Oh, Yu-Whan | - |
dc.contributor.author | Hwang, Sung Ho | - |
dc.date.accessioned | 2022-10-12T02:40:30Z | - |
dc.date.available | 2022-10-12T02:40:30Z | - |
dc.date.issued | 2022-09 | - |
dc.identifier.issn | 1011-8934 | - |
dc.identifier.issn | 1598-6357 | - |
dc.identifier.uri | https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/61589 | - |
dc.description.abstract | Background To propose fully automatic segmentation of left atrium using active learning with limited dataset in late gadolinium enhancement in cardiac magnetic resonance imaging (LGE-CMRI). Methods An active learning framework was developed to segment the left atrium in cardiac LGE-CMRI. Patients (n = 98) with atrial fibrillation from the Korea University Anam Hospital were enrolled. First, 20 cases were delineated for ground truths by two experts and used for training a draft model. Second, the 20 cases from the first step and 50 new cases, corrected in a human-in-the-loop manner after predicting using the draft model, were used to train the next model; all 98 cases (70 cases from the second step and 28 new cases) were trained. An additional 20 LGE-CMRI were evaluated in each step. Results The Dice coefficients for the three steps were 0.85 ± 0.06, 0.89 ± 0.02, and 0.90 ± 0.02, respectively. The biases (95% confidence interval) in the Bland-Altman plots of each step were 6.36% (−14.90–27.61), 6.21% (−9.62–22.03), and 2.68% (−8.57–13.93). Deep active learning-based annotation times were 218 ± 31 seconds, 36.70 ± 18 seconds, and 36.56 ± 15 seconds, respectively. Conclusion Deep active learning reduced annotation time and enabled efficient training on limited LGE-CMRI. | - |
dc.format.extent | 12 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 대한의학회 | - |
dc.title | Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imaging | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.doi | 10.3346/jkms.2022.37.e271 | - |
dc.identifier.scopusid | 2-s2.0-85138187527 | - |
dc.identifier.wosid | 000859314500002 | - |
dc.identifier.bibliographicCitation | Journal of Korean Medical Science, v.37, no.36, pp 1 - 12 | - |
dc.citation.title | Journal of Korean Medical Science | - |
dc.citation.volume | 37 | - |
dc.citation.number | 36 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 12 | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002876948 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | General & Internal Medicine | - |
dc.relation.journalWebOfScienceCategory | Medicine, General & Internal | - |
dc.subject.keywordAuthor | Active Learning | - |
dc.subject.keywordAuthor | Cardiac Image Analysis | - |
dc.subject.keywordAuthor | Convolutional Neural Network | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Human-in-the-Loop | - |
dc.subject.keywordAuthor | Magnetic Resonance Images | - |
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