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Using Machine Learning for Automatic Estimation of M. Smegmatis Cell Count from Fluorescence Microscopy Images

机译：使用机器学习从荧光显微镜图像自动估计耻垢分枝杆菌的细胞计数

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Relapse in Tuberculosis (TB) patients represents an important challenge to improve treatment. A large number of patients undergo relapse even after what was thought to be a successful treatment. Lipid rich (LR) bacteria, surviving treatment, are thought to play a key role in patient relapse. The presence of bacteria with intracellular lipid bodies in patients sputum was linked to higher risk of poor treatment outcome. LR bacteria can be stained and detected using fluorescence microscopy. However, manual counting of bacteria makes this method too labour intensive and potentially biased to be routinely used in practice or to foster large-scale data sets which would inform and drive future research efforts. In this paper we propose a new algorithm for automatic estimation of the number of bacteria present in images generated with fluorescence microscopy. Our approach comprises elements of image processing, computer vision and machine learning. We demonstrated the effectiveness of the method by testing it on fluorescence microscopy images of in vitro grown M. smegmatis cells stained with Nile red.
机译：结核病（TB）患者的复发是改善治疗的一项重要挑战。即使认为治疗成功后，仍有大量患者复发。存活下来的高脂类（LR）细菌被认为在患者复发中起关键作用。患者痰液中存在细胞内脂质体的细菌与治疗效果差的较高风险相关。 LR细菌可以染色并使用荧光显微镜检测。然而，细菌的手动计数使该方法过于费力，并且可能偏向于在实践中常规使用或无法建立大规模的数据集，而这将为将来的研究工作提供信息并推动其发展。在本文中，我们提出了一种新算法，用于自动估计荧光显微镜产生的图像中存在的细菌数量。我们的方法包括图像处理，计算机视觉和机器学习的元素。我们通过在体外培养的尼罗红染色的耻垢分枝杆菌细胞的荧光显微镜图像上测试该方法，证明了该方法的有效性。

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