Cited 3 times in
Impact of Denoising on Deep-Learning-Based Automatic Segmentation Framework for Breast Cancer Radiotherapy Planning
DC Field | Value | Language |
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dc.contributor.author | 성지원 | - |
dc.contributor.author | 이익재 | - |
dc.contributor.author | 이호 | - |
dc.date.accessioned | 2022-08-23T00:42:28Z | - |
dc.date.available | 2022-08-23T00:42:28Z | - |
dc.date.issued | 2022-07 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/189576 | - |
dc.description.abstract | Objective: This study aimed to investigate the segmentation accuracy of organs at risk (OARs) when denoised computed tomography (CT) images are used as input data for a deep-learning-based auto-segmentation framework. Methods: We used non-contrast enhanced planning CT scans from 40 patients with breast cancer. The heart, lungs, esophagus, spinal cord, and liver were manually delineated by two experienced radiation oncologists in a double-blind manner. The denoised CT images were used as input data for the AccuContourTM segmentation software to increase the signal difference between structures of interest and unwanted noise in non-contrast CT. The accuracy of the segmentation was assessed using the Dice similarity coefficient (DSC), and the results were compared with those of conventional deep-learning-based auto-segmentation without denoising. Results: The average DSC outcomes were higher than 0.80 for all OARs except for the esophagus. AccuContourTM-based and denoising-based auto-segmentation demonstrated comparable performance for the lungs and spinal cord but showed limited performance for the esophagus. Denoising-based auto-segmentation for the liver was minimal but had statistically significantly better DSC than AccuContourTM-based auto-segmentation (p < 0.05). Conclusions: Denoising-based auto-segmentation demonstrated satisfactory performance in automatic liver segmentation from non-contrast enhanced CT scans. Further external validation studies with larger cohorts are needed to verify the usefulness of denoising-based auto-segmentation. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | CANCERS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Impact of Denoising on Deep-Learning-Based Automatic Segmentation Framework for Breast Cancer Radiotherapy Planning | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiation Oncology (방사선종양학교실) | - |
dc.contributor.googleauthor | Jung Ho Im | - |
dc.contributor.googleauthor | Ik Jae Lee | - |
dc.contributor.googleauthor | Yeonho Choi | - |
dc.contributor.googleauthor | Jiwon Sung | - |
dc.contributor.googleauthor | Jin Sook Ha | - |
dc.contributor.googleauthor | Ho Lee | - |
dc.identifier.doi | 10.3390/cancers14153581 | - |
dc.contributor.localId | A06171 | - |
dc.contributor.localId | A03055 | - |
dc.contributor.localId | A03323 | - |
dc.relation.journalcode | J03449 | - |
dc.identifier.eissn | 2072-6694 | - |
dc.identifier.pmid | 35892839 | - |
dc.subject.keyword | contouring | - |
dc.subject.keyword | deep-learning-based auto-segmentation | - |
dc.subject.keyword | denoiser | - |
dc.subject.keyword | organs at risk | - |
dc.subject.keyword | radiation therapy | - |
dc.contributor.alternativeName | Sung, Jiwon | - |
dc.contributor.affiliatedAuthor | 성지원 | - |
dc.contributor.affiliatedAuthor | 이익재 | - |
dc.contributor.affiliatedAuthor | 이호 | - |
dc.citation.volume | 14 | - |
dc.citation.number | 15 | - |
dc.citation.startPage | 3581 | - |
dc.identifier.bibliographicCitation | CANCERS, Vol.14(15) : 3581, 2022-07 | - |
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