Automatic counting of microglial cell activation and its applications

Glaucoma is a multifactorial optic neuropathy characterized by the damage and death of the retinal ganglion cells.This disease results in vision loss and blindness.Any vision loss resulting from the disease cannot be restored and nowadays there is no available cure for glaucoma; however an early detection and treatment,could offer neuronal protection and avoid later serious damages to the visual function.A full understanding of the etiology of the disease will still require the contribution of many scientific efforts.Glial activation has been observed in glaucoma,being microglial proliferation a hallmark in this neurodegenerative disease.A typical project studying these cellular changes involved in glaucoma often needs thousands of images- from several animals- covering different layers and regions of the retina.The gold standard to evaluate them is the manual count.This method requires a large amount of time from specialized personnel.It is a tedious process and prone to human error.We present here a new method to count microglial cells by using a computer algorithm.It counts in one hour the same number of images that a researcher counts in four weeks,with no loss of reliability.

Development of automatic counting system for urediospores of wheat stripe rust based on image processing

To realize automatic counting of urediospores of Puccinia striiformis f.sp.tritici(Pst)(causal agent of wheat stripe rust),an automatic counting system for urediospores of wheat stripe rust pathogen based on image processing was developed using MATLAB GUIDE platform in combination with Local C Compiler(LCC).The system is independent of the MATLAB environment and can be run on a computer without the MATLAB software.Using this system,automatic counting of Pst urediospores in a microscopic image can be implemented via image processing technologies including image scaling,clustering segmentation,morphological modification,watershed transformation,connected region labeling,etc.Structure design of the automatic counting system,the key algorithms used in the system and realization of the main functions of the system were described in detail.Spore counting tests were conducted using microscopic digital images of Pst urediospores and the high accuracies more than 95%were obtained.The results indicated that it is feasible to count Pst urediospores automatically using the developed system based on image processing.

Deep learning method for cell count from transmitted-light microscope

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 applicability in clinical research.