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 ...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.展开更多
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 accu...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.展开更多
Red blood cell(RBC)counting is a standard medical test that can help diagnose various conditions and diseases.Manual counting of blood cells is highly tedious and time consuming.However,new methods for counting blood ...Red blood cell(RBC)counting is a standard medical test that can help diagnose various conditions and diseases.Manual counting of blood cells is highly tedious and time consuming.However,new methods for counting blood cells are customary employing both electronic and computer-assisted techniques.Image segmentation is a classical task in most image processing applications which can be used to count blood cells in a microscopic image.In this research work,an approach for erythrocytes counting is proposed.We employed a classification before counting and a new segmentation idea was implemented on the complex overlapping clusters in a microscopic smear image.Experimental results show that the proposed method is of higher counting accuracy and it performs much better than most counting algorithms existed in the situation of three or more RBCs overlapping complexly into a group.The average total erythrocytes counting accuracy of the proposed method reaches 92.9%.展开更多
基金supported by the Science Foundation of Arizona through the Bisgrove Program to PdG,Grant Number:BSP 0529-13the Ophthalmological Network OFTARED(RD12-0034/0002)+5 种基金the Institute of Health Carlos IIIthe PN I+D+i 2008–2011the ISCIII-Subdireccion General de Redes y Centros de Investigación Cooperativathe European Programme FEDERthe project SAF2014-53779-Rthe project:“The role of encapsulated NSAIDs in PLGA microparticles as a neuroprotective treatment” funded by the Spanish Ministry of Economy and Competitiveness
文摘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.
基金supported by the National Natural Science Foundation of China under Grant Nos.12274092,61871263,and 12034005partially by the Explorer Program of Shanghai under Grant No.21TS1400200+1 种基金partially by Natural Science Foundation of Shanghai under Grant No.21ZR1405200partially by Medical Engineering Fund of Fudan University under Grant No.YG2022-6.Mengyang Lu and Wei Shi contributed equally to this work.
文摘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.
基金This work was supported by the 863 National Plan Foundation of China under Grant No.2007AA01Z333Special Grand National Project of China under Grant No.2009ZX02204-008.
文摘Red blood cell(RBC)counting is a standard medical test that can help diagnose various conditions and diseases.Manual counting of blood cells is highly tedious and time consuming.However,new methods for counting blood cells are customary employing both electronic and computer-assisted techniques.Image segmentation is a classical task in most image processing applications which can be used to count blood cells in a microscopic image.In this research work,an approach for erythrocytes counting is proposed.We employed a classification before counting and a new segmentation idea was implemented on the complex overlapping clusters in a microscopic smear image.Experimental results show that the proposed method is of higher counting accuracy and it performs much better than most counting algorithms existed in the situation of three or more RBCs overlapping complexly into a group.The average total erythrocytes counting accuracy of the proposed method reaches 92.9%.
