Journal of Innovative Optical Health Sciences最新文献

A 3D semantic segmentation network for accurate neuronal soma segmentation 用于精确神经元体节分割的三维语义分割网络

IF 2.3 3区医学

Journal of Innovative Optical Health Sciences Pub Date : 2024-07-19 DOI: 10.1142/s1793545824500184

Li Ma, Qi Zhong, Yezi Wang, Xiaoquan Yang, Qian Du

引用次数: 0

In vivo fluorescence flow cytometry reveals that the nanoparticle tumor vaccine OVA@HA-PEI effectively clears circulating tumor cells 体内荧光流式细胞仪显示，纳米颗粒肿瘤疫苗 OVA@HA-PEI 能有效清除循环肿瘤细胞

IF 2.3 3区医学

Journal of Innovative Optical Health Sciences Pub Date : 2024-07-19 DOI: 10.1142/s1793545824500172

Wei Jin, Yuting Fu, Sisi Ge, Han Sun, K. Pang, Xunbin Wei

引用次数: 0

Multi-class classification of pathological myopia based on fundus photography 根据眼底摄影对病理性近视进行多级分类

IF 2.3 3区医学

Journal of Innovative Optical Health Sciences Pub Date : 2024-07-19 DOI: 10.1142/s1793545824500160

Jiaqing Zhao, Guogang Cao, Jiangnan He, Cuixia Dai

引用次数: 0

Recent Advances in Near-infrared Photobiomodulation for the Intervention of Acquired Brain Injury 用于后天性脑损伤干预的近红外光生物调制技术的最新进展

IF 2.3 3区医学

Journal of Innovative Optical Health Sciences Pub Date : 2024-07-19 DOI: 10.1142/s1793545824300052

Yujing Huang, Yujing Zhang, Chen Yang, Mengze Xu, Zhen Yuan

引用次数: 0

A generalized deep neural network approach for improving resolution of fluorescence microscopy images 提高荧光显微镜图像分辨率的广义深度神经网络方法

IF 2.3 3区医学

Journal of Innovative Optical Health Sciences Pub Date : 2024-07-10 DOI: 10.1142/s1793545824500111

Zichen Jin, Qing He, Yang Liu, Kaige Wang

{"title":"A generalized deep neural network approach for improving resolution of fluorescence microscopy images","authors":"Zichen Jin, Qing He, Yang Liu, Kaige Wang","doi":"10.1142/s1793545824500111","DOIUrl":"https://doi.org/10.1142/s1793545824500111","url":null,"abstract":"Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed, imaging resolution, and imaging flux. This paper proposes a deep neural network based on a generative adversarial network (GAN). The generator employs a U-Net-based network, which integrates DenseNet for the downsampling component. The proposed method has excellent properties, for example, the network model is trained with several different datasets of biological structures; the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging; and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets. In addition, experimental results showed that the method improved the resolution of caveolin-coated pits (CCPs) structures from 264[Formula: see text]nm to 138[Formula: see text]nm, a 1.91-fold increase, and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.","PeriodicalId":16248,"journal":{"name":"Journal of Innovative Optical Health Sciences","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141835790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Label-free in-vivo classification and tracking of red blood cells and platelets using Dynamic-YOLOv4 network 使用动态-YOLOv4 网络对红细胞和血小板进行无标记体内分类和跟踪

IF 2.5 3区医学

Journal of Innovative Optical Health Sciences Pub Date : 2024-05-25 DOI: 10.1142/s1793545824500093

Caizhong Guan, Bin He, Hongting Zhang, Shangpan Yang, Yang Xu, Honglian Xiong, Yaguang Zeng, Mingyi Wang, Xunbin Wei

{"title":"Label-free in-vivo classification and tracking of red blood cells and platelets using Dynamic-YOLOv4 network","authors":"Caizhong Guan, Bin He, Hongting Zhang, Shangpan Yang, Yang Xu, Honglian Xiong, Yaguang Zeng, Mingyi Wang, Xunbin Wei","doi":"10.1142/s1793545824500093","DOIUrl":"https://doi.org/10.1142/s1793545824500093","url":null,"abstract":"In-vivo flow cytometry is a noninvasive real-time diagnostic technique that facilitates continuous monitoring of cells without perturbing their natural biological environment, which renders it a valuable tool for both scientific research and clinical applications. However, the conventional approach for improving classification accuracy often involves labeling cells with fluorescence, which can lead to potential phototoxicity. This study proposes a label-free in-vivo flow cytometry technique, called dynamic YOLOv4 (D-YOLOv4), which improves classification accuracy by integrating absorption intensity fluctuation modulation (AIFM) into YOLOv4 to demodulate the temporal features of moving red blood cells (RBCs) and platelets. Using zebrafish as an experimental model, the D-YOLOv4 method achieved average precisions (APs) of 0.90 for RBCs and 0.64 for thrombocytes (similar to platelets in mammals), resulting in an overall AP of 0.77. These scores notably surpass those attained by alternative network models, thereby demonstrating that the combination of physical models with neural networks provides an innovative approach toward developing label-free in-vivo flow cytometry, which holds promise for diverse in-vivo cell classification applications.","PeriodicalId":16248,"journal":{"name":"Journal of Innovative Optical Health Sciences","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exhaustive Review of Acceleration Strategies for Monte Carlo Simulations in Photon Transit 光子过境蒙特卡洛模拟加速策略详尽评述

IF 2.5 3区医学

Journal of Innovative Optical Health Sciences Pub Date : 2024-05-23 DOI: 10.1142/s1793545824300040

Louzhe Xu, Zijie Zhu, Ting Li

引用次数: 0

Unified deep learning model for predicting fundus fluorescein angiography image from fundus structure image 从眼底结构图像预测眼底荧光素血管造影图像的统一深度学习模型

IF 2.5 3区医学

Journal of Innovative Optical Health Sciences Pub Date : 2024-03-22 DOI: 10.1142/s1793545824500032

Yiwei Chen, Yi He, Hong Ye, Lina Xing, Xin Zhang, Guohua Shi

{"title":"Unified deep learning model for predicting fundus fluorescein angiography image from fundus structure image","authors":"Yiwei Chen, Yi He, Hong Ye, Lina Xing, Xin Zhang, Guohua Shi","doi":"10.1142/s1793545824500032","DOIUrl":"https://doi.org/10.1142/s1793545824500032","url":null,"abstract":"The prediction of fundus fluorescein angiography (FFA) images from fundus structural images is a cutting-edge research topic in ophthalmological image processing. Prediction comprises estimating FFA from fundus camera imaging, single-phase FFA from scanning laser ophthalmoscopy (SLO), and three-phase FFA also from SLO. Although many deep learning models are available, a single model can only perform one or two of these prediction tasks. To accomplish three prediction tasks using a unified method, we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network. The three prediction tasks are processed as follows: data preparation, network training under FFA supervision, and FFA image prediction from fundus structure images on a test set. By comparing the FFA images predicted by our model, pix2pix, and CycleGAN, we demonstrate the remarkable progress achieved by our proposal. The high performance of our model is validated in terms of the peak signal-to-noise ratio, structural similarity index, and mean squared error.","PeriodicalId":16248,"journal":{"name":"Journal of Innovative Optical Health Sciences","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Review of polarization-based technology for biomedical applications 基于偏振技术的生物医学应用回顾

IF 2.5 3区医学

Journal of Innovative Optical Health Sciences Pub Date : 2024-03-22 DOI: 10.1142/s1793545824300027

Caizhong Guan, Nan Zeng, Honghui He

引用次数: 0

Automatic detection method of bladder tumor cells based on color and shape features 基于颜色和形状特征的膀胱肿瘤细胞自动检测方法

IF 2.5 3区医学

Journal of Innovative Optical Health Sciences Pub Date : 2024-03-13 DOI: 10.1142/s1793545824500056

Zitong Zhao, Yanbo Wang, Jiaqi Chen, Mingjia Wang, Shulong Feng, Jin Yang, Nan Song, Jinyu Wang, Ci Sun

引用次数: 0