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    基于深度学习与阈值分割的线虫识别

    Application of deep learning and threshold segmentation in nematode recognition

    • 摘要: 目的 基于深度学习和阈值分割的方法探索对图像中秀丽隐杆线虫数量和聚集程度的统计是否能提高行为实验的效率和准确度,便于利用线虫等小型模式生物高通量筛选针对孤独症的潜在药物。方法 采集5 000张含有单只线虫的培养基样本,分别用人工计数法和基于深度学习与阈值分割的方法设计线虫识别检测算法,实现对培养基中线虫的计数与聚集程度判断,并对比其耗时长短和准确率。结果 本研究基于深度学习和阈值分割算法,设计了自动统计线虫数量及聚集程度的软件,该软件对每张图像的检测时间在毫秒级别,相比较人工统计的100 s量级,检测效率显著提高,且检测的准确率较好,针对单只线虫的检测准确率超过90%。结论 与人工计数法相比,基于深度学习与阈值分割算法的软件对模式生物秀丽隐杆线虫计数和聚集程度判断的效率明显提高,本研究的基于深度学习与阈值分割算法将显著提高孤独症等神经行为相关药物筛选效率。

       

      Abstract: Objective To explore whether deep learning and threshold segmentation based methods can improve the efficiency and accuracy of behavioral experiments to realize the statistics of the number and aggregation degree of Caenorhabditis elegans in images, and facilitate the high-throughput screening of potential drugs for autism using nematodes and other small model organisms. Methods A total of 5000 samples of culture media containing single nematodes were collected. Then nematode recognition and detection algorithms were designed through manual counting, and deep learning and threshold segmentation method, respectively, so as to judge the count and aggregation degree of nematodes in culture media. Meanwhile, analysis time and accurate rate were compared. Results Based on deep learning and threshold segmentation algorithm, we designed a software that automatically calculated the quantity of nematodes and aggregation degree. The software was able to detect each image within milliseconds, with a remarkably increased detection rate, compared with 100 seconds or more spent for artificial statistics. The software also exhibited good detection accuracy, with over 90% accuracy for single worm detection. Conclusions Compared with manual counting, the software based on deep learning and threshold segmentation algorithm can significantly improve the efficiency of Caenorhabditis elegans counting and aggregation degree judgment. The algorithm based on deep learning and threshold segmentation in this study will significantly improve the screening efficiency of neurobehavioral drugs related to autism.

       

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