https://scholars.lib.ntu.edu.tw/handle/123456789/636596
標題: | Self-Training with High-Dimensional Markers for Cell Instance Segmentation | 作者: | Lo, Kuang Cheng Lin, Cheng Wei Lee, Hsin Ying Hsu, Hao WINSTON HSU TUNG-HUNG SU Chen, Shih Yu YUNG-MING JENG |
關鍵字: | Cell segmentation | CODEX | deep learning | highly-multiplexed imaging | self-training | 公開日期: | 1-一月-2023 | 卷: | 2023-April | 來源出版物: | Proceedings - International Symposium on Biomedical Imaging | 摘要: | Cellular segmentation is a fundamental prerequisite to many biological analyses. With the development of multiplexed imaging technologies, the need for accurately segmenting individual cells has significantly increased in recent years. However, current deep learning methods cannot deal with staining markers in an arbitrary order or different numbers. Moreover, acquiring pixel-level annotation is incredibly time-consuming in high-dimensional images. To tackle these issues, we incorporate pathology knowledge into our model and present a novel self-training framework. Concretely, we apply a serial attention mechanism and pooling operation to compress the multi-channel image during the training process. Afterward, the nuclei information guides the self-training in the pseudo-label stage. Experiments demonstrate our method is superior to the existing methods in both qualitative and quantitative results. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/636596 | ISBN: | 9781665473583 | ISSN: | 19457928 | DOI: | 10.1109/ISBI53787.2023.10230837 |
顯示於: | 資訊工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。