All You Need Is a Few Dots to Label CT Images for Organ Segmentation
- Title
- All You Need Is a Few Dots to Label CT Images for Organ Segmentation
- Author
- 김영훈
- Keywords
- medical image segmentation; CT image segmentation; deep learning; kernel density; semi-automated labeling tool
- Issue Date
- 2022-01
- Publisher
- MDPI
- Citation
- APPLIED SCIENCES-BASEL, v. 12, NO 3, Page. 1328-1340
- 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.
- URI
- https://www.proquest.com/docview/2636121787?accountid=11283https://repository.hanyang.ac.kr/handle/20.500.11754/171076
- ISSN
- 20763417
- DOI
- 10.3390/app12031328
- Appears in Collections:
- ETC[S] > 연구정보
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML