论文标题
DIBA数据集的Gram染色显微镜图像的半自动标记和语义分割
Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset
论文作者
论文摘要
在本文中，使用聚类和阈值算法实现了DIBA数据集细菌属和物种的半自动注释。训练了深度学习模型，以实现细菌物种的语义分割和分类。分类精度达到95％。深度学习模型在生物医学图像处理中发现了巨大的应用。从革兰氏阴性的显微镜图像中自动分割细菌对于诊断呼吸道和尿路感染，检测癌症等至关重要。深度学习将有助于生物学家在更少的时间内获得可靠的结果。此外，可以减少许多人类干预措施。这项工作可能有助于检测尿涂片图像，痰液涂片图像等的细菌，以诊断尿路感染，结核病，肺炎等。
In this paper, a semi-automatic annotation of bacteria genera and species from DIBaS dataset is implemented using clustering and thresholding algorithms. A Deep learning model is trained to achieve the semantic segmentation and classification of the bacteria species. Classification accuracy of 95% is achieved. Deep learning models find tremendous applications in biomedical image processing. Automatic segmentation of bacteria from gram-stained microscopic images is essential to diagnose respiratory and urinary tract infections, detect cancers, etc. Deep learning will aid the biologists to get reliable results in less time. Additionally, a lot of human intervention can be reduced. This work can be helpful to detect bacteria from urinary smear images, sputum smear images, etc to diagnose urinary tract infections, tuberculosis, pneumonia, etc.
