Interactive Cell Segmentation Based on Correction Propagation
Automatic cell segmentation can hardly be flawless due to the complexity of image data particularly when time-lapse experiments last for a long time without biomarkers. To address this issue, we propose an interactive cell segmentation method that actively selects uncertain regions and requests human validation on them. Once erroneous segmentation is detected and subsequently corrected, the information is propagated over affinity graphs in order to fix analogous errors. We present a systematical method for correction propagation based on active and semi-supervised learning. Experimental results performed on three types of cell populations validate that our interactive cell segmentation quickly reaches high quality results with minimal human interventions, and thus is significantly more efficient than alternative methods.
H. Su et al., "Interactive Cell Segmentation Based on Correction Propagation," Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (2014, Beijing, China), pp. 1381-1384, Institute of Electrical and Electronics Engineers (IEEE), Jul 2014.
The definitive version is available at https://doi.org/10.1109/ISBI.2014.6868135
2014 IEEE 11th International Symposium on Biomedical Imaging (2014: Apr. 29-May 2, Beijing, China)
Keywords and Phrases
Cell Culture; Cell Proliferation; Cells; Cytology; Medical Imaging; Supervised Learning; Cell Populations; Cell Segmentation; High Quality; Human Intervention; Image Data; Semi-Supervised Learning; Image Segmentation; Active Learning; Correction Propagation; Interactive Correction
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
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