Morphological Components Detection for Super-Depth-of-Field Bio-Micrograph Based on Deep Learning.

Accompanied with the clinical routine examination demand increase sharply, the efficiency and accuracy is the first priority. However, the issues on the automatic detection and localization of cells in microscopic images of specific biological sample in super-depth-of-field (SDoF) system remains great challenges. We proposed object detection method for cells and other phenotypic components in the SDoF micrograph based on Retinanet. Compared the current mainstream algorithm, the mean average precision (mAP) index is significantly improved. In the experiment of leucorrhea samples and fecal samples, mAP indexes are 83.1% and 88.1% respectively, with an average increase of 10%. These improved characteristics can put the efficiency and accuracy forward significantly. The algorithm proposed in this paper can be integrated into the fecal and leucorrhea automatic detection system.