Automatic bright-field smear microscopy for diagnosis of pulmonary tuberculosis,Computers in Biology and Medicine

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Automatic bright-field smear microscopy for diagnosis of pulmonary tuberculosis
Computers in Biology and Medicine ( IF 7.0 ) Pub Date : 2024-02-29 , DOI: 10.1016/j.compbiomed.2024.108167
Mikaela Kalline Maciel Serrão ₁ , Marly Guimarães Fernandes Costa ₁ , Luciana Botinelly Mendonça Fujimoto ₂ , Mauricio Morishi Ogusku ₃ , Cicero Ferreira Fernandes Costa Filho ₁

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In recent decades, many studies have been published on the use of automatic smear microscopy for diagnosing pulmonary tuberculosis (TB). Most of them deal with a preliminary step of the diagnosis, the bacilli detection, whereas sputum smear microscopy for diagnosis of pulmonary TB comprises detecting and reporting the number of bacilli found in at least 100 microscopic fields, according to the 5 grading scales (negative, scanty, 1+, 2+ and 3+) endorsed by the World Health Organization (WHO). Pulmonary TB diagnosis in bright-field smear microscopy, however, depends upon the attention of a trained and motivated technician, while the automated TB diagnosis requires little or no interpretation by a technician. As far as we know, this work proposes the first automatic method for pulmonary TB diagnosis in bright-field smear microscopy, according to the WHO recommendations. The proposed method comprises a semantic segmentation step, using a deep neural network, followed by a filtering step aiming to reduce the number of false positives (false bacilli): color and shape filtering. In semantic segmentation, different configurations of encoders are evaluated, using depth-wise separable convolution layers and channel attention mechanism. The proposed method was evaluated with a large, robust, and annotated image dataset designed for this purpose, consisting of 250 testing sets, 50 sets for each of the 5 TB diagnostic classes. The following performance metrics were obtained for automatic pulmonary TB diagnosis by smear microscopy: mean precision of 0.894, mean recall of 0.896, and mean F1-score of 0.895.

中文翻译：

自动明视野涂片显微镜诊断肺结核

近几十年来，许多关于使用自动涂片显微镜诊断肺结核（TB）的研究已发表。其中大多数涉及诊断的初步步骤，即杆菌检测，而用于诊断肺结核的痰涂片显微镜检查包括根据 5 个分级标准（阴性、阴性、很少、1+、2+ 和 3+）得到了世界卫生组织 (WHO) 的认可。然而，明场涂片显微镜下的肺结核诊断取决于训练有素且积极主动的技术人员的注意力，而自动化结核病诊断则很少或不需要技术人员的解释。据我们所知，根据世界卫生组织的建议，这项工作提出了第一个在明场涂片显微镜下自动诊断肺结核的方法。所提出的方法包括使用深度神经网络的语义分割步骤，以及旨在减少误报（假杆菌）数量的过滤步骤：颜色和形状过滤。在语义分割中，使用深度可分离卷积层和通道注意机制来评估编码器的不同配置。使用为此目的设计的大型、稳健且带注释的图像数据集对所提出的方法进行了评估，该数据集包含 250 个测试集，每个 5 TB 诊断类别有 50 个测试集。通过涂片显微镜自动诊断肺结核获得了以下性能指标：平均精度为 0.894，平均召回率为 0.896，平均 F1 分数为 0.895。

更新日期：2024-02-29

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