Current Result Document :
| 한글제목(Korean Title) |
심층 시멘틱 세그멘테이션을 활용한 전체 조직 슬라이드 이미지의 품질 관리 |
| 영문제목(English Title) |
Quality Control of Whole Slide Pathology Images with Deep Semantic Segmentation |
| 저자(Author) |
이정현
윤세영
Junghyun Lee
Se-Young Yun
마해 자빈
최호진
정유채
Mahe Zabin
Ho-Jin Choi
Yuchae Jung
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| 원문수록처(Citation) |
VOL 49 NO. 02 PP. 0660 ~ 0662 (2022. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
During routine slide preparation introduction of batch effects and artefacts are commonplace. The routine of slide preparation can result in tissue folds and unwanted artefacts. The digitization process may introduce blurring and contrast issues in brightness levels. Numerous computational tools are used for slide analysis, with a limited focus on quality issues. Semantic Segmentation is a deep learning technique used to map pixels of an image to specific labels. External artefacts present in a whole slide image introduce texture and color variations that can be generalized by deep learning architectures. The research proposes a standard UNet architecture to segment pen marks and regions where tissues appear to be folded. Experimental results show that the proposed method exhibits an average of 0.97 Intersection over Union (IOU) for the dataset. |
| 키워드(Keyword) |
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| 파일첨부 |
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