Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김영훈 | - |
dc.date.accessioned | 2022-05-23T06:52:52Z | - |
dc.date.available | 2022-05-23T06:52:52Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.citation | APPLIED SCIENCES-BASEL, v. 12, NO 3, Page. 1328-1340 | en_US |
dc.identifier.issn | 20763417 | - |
dc.identifier.uri | https://www.proquest.com/docview/2636121787?accountid=11283 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/171076 | - |
dc.description.abstract | Image segmentation is used to analyze medical images quantitatively for diagnosis and treatment planning. Since manual segmentation requires considerable time and effort from experts, research to automatically perform segmentation is in progress. Recent studies using deep learning have improved performance but need many labeled data. Although there are public datasets for research, manual labeling is required in an area where labeling is not performed to train a model. We propose a deep-learning-based tool that can easily create training data to alleviate this inconvenience. The proposed tool receives a CT image and the pixels of organs the user wants to segment as inputs and extract the features of the CT image using a deep learning network. Then, pixels that have similar features are classified to the identical organ. The advantage of the proposed tool is that it can be trained with a small number of labeled data. After training with 25 labeled CT images, our tool shows competitive results when it is compared to the state-of-the-art segmentation algorithms, such as UNet and DeepNetV3. | en_US |
dc.description.sponsorship | This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government (Ministry of Science and ICT (MSIT)) through the Artificial Intelligence Convergence Research Center (Hanyang University ERICA), under Grant 2020-0-01343, and in part by the National Research Foundation of Korea (NRF) funded by the Korea Government (MSIT) under Grant 2020R1G1A1011471. | en_US |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.subject | medical image segmentation | en_US |
dc.subject | CT image segmentation | en_US |
dc.subject | deep learning | en_US |
dc.subject | kernel density | en_US |
dc.subject | semi-automated labeling tool | en_US |
dc.title | All You Need Is a Few Dots to Label CT Images for Organ Segmentation | en_US |
dc.type | Article | en_US |
dc.relation.no | 3 | - |
dc.relation.volume | 12 | - |
dc.identifier.doi | 10.3390/app12031328 | - |
dc.relation.page | 1328-1340 | - |
dc.relation.journal | APPLIED SCIENCES-BASEL | - |
dc.contributor.googleauthor | Ju, Mingeon | - |
dc.contributor.googleauthor | Lee, Moonhyun | - |
dc.contributor.googleauthor | Lee, Jaeyoung | - |
dc.contributor.googleauthor | Yang, Jaewoo | - |
dc.contributor.googleauthor | Yoon, Seunghan | - |
dc.contributor.googleauthor | Kim, Younghoon | - |
dc.relation.code | 2022039077 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF COMPUTING[E] | - |
dc.sector.department | DEPARTMENT OF ARTIFICIAL INTELLIGENCE | - |
dc.identifier.pid | nongaussian | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.