Deep learning method for cell count from transmitted-light microscope

Journal of Innovative Optical Health Sciences, Ahead of Print. Automatic cell counting provides an effective tool for medical research and diagnosis. Currently, cell counting can be completed by transmitted-light microscope, however, it requires expert knowledge and the counting accuracy which is unsatisfied for overlapped cells. Further, the image-translation-based detection method has been proposed and the potential has been shown to accomplish cell counting from transmitted-light microscope, automatically and effectively. In this work, a new deep-learning (DL)-based two-stage detection method (cGAN-YOLO) is designed to further enhance the performance of cell counting, which is achieved by combining a DL-based fluorescent image translation model and a DL-based cell detection model. The various results show that cGAN-YOLO can effectively detect and count some different types of cells from the acquired transmitted-light microscope images. Compared with the previously reported YOLO-based one-stage detection method, high recognition accuracy (RA) is achieved by the cGAN-YOLO method, with an improvement of 29.80%. Furthermore, we can also observe that cGAN-YOLO obtains an improvement of 12.11% in RA compared with the previously reported image-translation-based detection method. In a word, cGAN-YOLO makes it possible to implement cell counting directly from the experimental acquired transmitted-light microscopy images with high flexibility and performance, which extends the appli...
Source: Journal of Innovative Optical Health Sciences - Category: Biomedical Science Authors: Source Type: research