arXiv cs.AI· Truong X. Tran, Marc L. Pusey, Ramazan S. Aygun·· 3 天前AI 评分12
利用荧光显微图像中的从属颜色通道进行蛋白质结晶分割与分类
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images
AI 导读
针对痕量荧光标记（TFL）蛋白质结晶试验图像，研究发现使用发射光谱峰值波长之外的从属颜色通道强度，可将似先导物或晶体被误判为非晶体的比例从9.71%降至2.02%，分类器准确率提升1.77%至5.53%。
正文 · 原文
Abstract:The accuracy of detecting protein crystals for fluorescence microscopy images is very critical for high throughput and automated systems. Although the trace fluorescent labeling method could highlight protein crystals, reflection and emission from the fluorescence dye is not always due to crystal regions. Therefore, the analysis of the peak wavelength in the emission spectra of a fluorophore may not always yield effective results. In this paper, we show that using the subordinate color intensity corresponding to longer wavelengths than the peak wavelength of the emission spectra could improve the accuracy of protein crystal detection. Hence, we have built a segmentation method based on the percentile intensity of the subordinate color for trace fluorescently labeled (TFL'd) protein crystallization trial images. Compared to using the dominant color channel, our segmentation method on subordinate color channel was able to reduce the misclassification rate of likely-leads or crystals as non-crystals by the percentage of from 9.71% to 2.02% depending on the classifier. Similarly, the accuracy of classifiers were increased by the percentage of from 1.77% to 5.53%. Our method reached around 94% accuracy while keeping misclassification of likely-leads and crystals as non-crystals below 1%. Moreover, to evaluate the generalizability of our method, we have conducted new wet lab experiments on two proteins, Concanavalin A (Con A) and Ab inorganic pyrophosphate (AbIPPase), and the misclassification rate was below 1%. Our experiments show that using the subordinate channel may be more helpful for TFL'd protein trial image classification.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.15286 [cs.CV] |
| (or arXiv:2610.15286v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15286 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Journal of Fluorescence, vol. 30, pp. 637-656, May 2020 |
| Related DOI: | https://doi.org/10.1007/s10895-020-02500-7
DOI(s) linking to related resources |
Submission history
From: Truong Tran [view email]
[v1]
Sun, 11 Oct 2026 02:33:49 UTC (9,826 KB)
来源：arXiv cs.AI · arxiv.org