Ferrari Alessandro, Lombardi Stefano, Signoroni Alberto
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7458-61. doi: 10.1109/EMBC.2015.7320116.
Counting bacterial colonies on microbiological culture plates is a time-consuming, error-prone, nevertheless fundamental task in microbiology. Computer vision based approaches can increase the efficiency and the reliability of the process, but accurate counting is challenging, due to the high degree of variability of agglomerated colonies. In this paper, we propose a solution which adopts Convolutional Neural Networks (CNN) for counting the number of colonies contained in confluent agglomerates, that scored an overall accuracy of the 92.8% on a large challenging dataset. The proposed CNN-based technique for estimating the cardinality of colony aggregates outperforms traditional image processing approaches, becoming a promising approach to many related applications.
在微生物培养平板上计数细菌菌落是微生物学中一项耗时、易出错但又至关重要的任务。基于计算机视觉的方法可以提高该过程的效率和可靠性，但由于聚集菌落的高度变异性，准确计数具有挑战性。在本文中，我们提出了一种解决方案，该方案采用卷积神经网络（CNN）来计数融合聚集体中包含的菌落数量，在一个具有挑战性的大型数据集上总体准确率达到了92.8%。所提出的基于CNN的估计菌落聚集体基数的技术优于传统图像处理方法，成为许多相关应用的一种有前途的方法。
