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Deep learning-assisted reconstruction of Raman speckle images transmitted through biological tissues 认领 引用 被引量：1
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作者 Haoqiang Xie Erjia Wang +3 位作者 Zhouzhou Bao Zhou Chen Linley Li Lin Jian Ye 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第4期177-190,共14页
Precise delineation of tumor tissues during surgery is hindered by the strong scattering and absorption of photons in biological tissues,which obscure molecular imaging signals and hinder intraoperative navigation.Ram... Precise delineation of tumor tissues during surgery is hindered by the strong scattering and absorption of photons in biological tissues,which obscure molecular imaging signals and hinder intraoperative navigation.Raman bioimaging using Surface-Enhanced Raman Scattering(SERS)nanoprobes offers high molecular specificity and sensitivity but still suffers from severe degradation when the signals are transmitted through scattering media.Here,we present a deep learning-assisted approach for reconstructing Raman speckle images obscured by biological scattering.We synthesized Gap-Enhanced Resonance Raman Tags(GERRTs)and used them to draw Raman patterns on paper.These patterns were tested to build the dataset of image pairs,including reference Raman images that were collected directly from the patterns and speckle Raman images after signal transmission through 4mm porcine tissues.To recover these images,we employed a U-Net architecture featuring symmetric encoder–decoder pathways with skip connections,which effectively preserves high-resolution spatial details lost in scattering tissue.This model achieved superior reconstruction performance,with an average Structural Similarity Index Measure(SSIM)of 0.539 between reconstructed images and reference images,and a Peak Signal-to-Noise Ratio(PSNR)of 19.04 dB.This work provides a proof-of-concept framework for deep learning-assisted Raman imaging through scattering tissues and holds promise for advancing noninvasive optical molecular imaging and facilitating precise surgical navigation in oncology. 展开更多
关键词 Raman imaging speckle reconstruction deep learning U-Net
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Research Review of Deep Learning Algorithms for Agricultural Disease Image Classification 认领 引用
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作者 Shengjiu JIANG Qian WANG 《Plant Diseases and Pests》 2026年第1期30-34,共5页
In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention a... In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention and control of agricultural diseases. This paper provides a systematic review of the evolutionary development of algorithms within this field. Addressing challenges such as domain drift and limited global awareness in classical convolutional neural networks (CNNs) applied to complex agricultural environments, the paper focuses on the latest advancements in vision transformers (ViT) and their hybrid architectures to enhance cross-domain robustness and fine-grained recognition capabilities. In response to the challenges posed by scarce long-tail data and limited edge computing power in real-world scenarios, the paper explores solutions related to few-shot learning and ultra-lightweight network deployment. Finally, a forward-looking analysis is presented on the application paradigms of multimodal feature fusion, vision-based large models, and explainable artificial intelligence (AI) within smart plant protection. This analysis aims to offer theoretical insights for the development of efficient and transparent intelligent diagnostic systems for agricultural diseases, thereby supporting the advancement of digital agriculture and the construction of a robust agricultural nation. 展开更多
关键词 Agricultural disease image Classification algorithm Deep learning Research Review
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Reducing Both Radiation Dose and Iodine Intake in 80 kVp Head and Neck CT Angiography Using Deep Learning Image Reconstruction Combined with Contrast-Enhancement-boost Technology:A Comparison with 100 kVp Imaging Using Hybrid Iterative Reconstruction 认领 引用
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作者 WANG Yun ZHANG Xinyue +5 位作者 TONG Jiajing CHEN Yu XU Min WANG Jian ZHANG Zhuhua JIN Zhengyu 《CT理论与应用研究（中英文）》 2025年第6期1082-1091,共10页
Purpose:To assess the clinical efficacy of integrating deep learning reconstruction(DLR)with contrast-enhancement-boost(CE-boost)in 80 kVp head and neck CT angiography(CTA)using substantially lowered radiation and con... Purpose:To assess the clinical efficacy of integrating deep learning reconstruction(DLR)with contrast-enhancement-boost(CE-boost)in 80 kVp head and neck CT angiography(CTA)using substantially lowered radiation and contrast medium(CM)doses,compared to the standard 100 kVp protocol using hybrid iterative reconstruction(HIR).Methods:Sixty-six patients were prospectively enrolled and randomly assigned to one of two groups:the low-dose group(n=33),receiving 80 kVp and 28 mL contrast medium(CM)with a noise index(NI)of 15;and the regular-dose group(n=33),receiving 100 kVp and 40 mL CM with an NI of 10.For the lowdose group,images underwent reconstruction using both hybrid iterative reconstruction(HIR)and deep learning reconstruction(DLR)at mild-,standard-,and strong-strength levels,both before and after combination with contrast enhancement-boost(CE-boost).This generated eight distinct datasets:L-HIR,L-DLRmild,L-DLRstandard,L-DLRstrong,L-HIR-CE,L-DLRmild-CE,L-DLRstandard-CE,and L-DLRstrong-CE.Images for the regular-dose group were reconstructed solely with HIR(R-HIR).Quantitative analysis involved calculating and comparing CT attenuation,image noise,signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR)within six key vessels:the aortic arch(AA),internal carotid artery(ICA),external carotid artery(ECA),vertebral arteries(VA),basilar artery(BA),and middle cerebral artery(MCA).Two radiologists independently assessed subjective image quality using a 5-point scale,with statistical significance defined as P<0.05.Results:Compared to the regular-dose group,the low-dose protocol achieved a substantial reduction in contrast media volume(28 mL versus 40 mL,a 30%decrease)and radiation exposure((0.41±0.08)mSv versus(1.18±0.12)mSv,a 65%reduction).Both L-DLRstandard and L-DLRstrong delivered comparable or superior SNR and CNR across all vascular segments relative to R-HIR.However,subjective image quality scores for L-DLR at all strength levels fell below those for R-HIR(all P<0.05 for both readers).Combining CE-boost with the low-dose protocol significantly enhanced the objective image performance of L-DLRstrong-CE(all P<0.05)and produced subjective image scores comparable to R-HIR(reader 1:P=0.15;reader 2:P=0.06).Conclusion:When compared to the standard 100 kVp head and neck CTA,the combination of the DLR and CE-boost techniques at 80 kVp can achieve a 30%reduction in contrast dose and a 65%reduction in radiation dose,while maintaining both objective and subjective image quality. 展开更多
关键词 computed tomography angiography radiation dosage deep learning reconstruction image quality
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Image Quality Optimization in 60 kVp Head-Neck CTA:A Comparative Study of FBP,ClearView,and ClearInfinity Reconstruction Algorithms 认领 引用
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作者 Shao-fang Wang Zhen Li +6 位作者 Li-hui Dai Huan Liu Yan-qiu Zhang Yan Huang Xiang-yue Zha Jing Zhang Qiu-xia Wang 《Current Medical Science》 SCIE CAS CSCD 2025年第6期1504-1512,共9页
Objective To compare the impact of different reconstruction algorithms on the image quality of 60 kVp head and neck CT angiography(CTA)using subjective and objective metrics,with a focus on vessel edge sharpness.Metho... Objective To compare the impact of different reconstruction algorithms on the image quality of 60 kVp head and neck CT angiography(CTA)using subjective and objective metrics,with a focus on vessel edge sharpness.Methods This prospective study enrolled 45 patients who underwent ultra-low-voltage(60 kVp)head and neck CTA.Image datasets were reconstructed with filtered back-projection(FBP),ClearView(CV)and ClearInfinity(CI)algorithms at low(30%),medium(50%),and high(70%)strengths.Image quality was assessed subjectively and objectively via the Kruskal‒Wallis test for multiple comparisons.Objective parameters,including edge rise slope(ERS)and edge rise distance(ERD),were analyzed via the Friedman test of multiple comparisons statistics.Results Subjective assessments favored the CI50 reconstruction algorithm,demonstrating superior or satisfactory results compared to the other algorithms,with significantly better vessel delineation,edge definition and diagnostic confidence(all P0.05)across all evaluated head and neck vascular segments when compared with the FBP algorithm.Conclusions The CI50 reconstruction algorithm optimizes image quality in 60 kVp head and neck CTA.It provides vessel edge sharpness comparable to FBP while offering superior vessel delineation,edge definition,and diagnostic confidence compared to FBP and CV algorithm.These findings suggest that CI50 has the potential to improve diagnostic accuracy in low-dose vascular imaging. 展开更多
关键词 Computed tomography angiography Reconstruction algorithm Deep learning reconstruction Low-dose CT Image quality Vessel sharpness 60 kVp Heal-neck imaging
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Deep learning-driven computational imaging for light field microscopy 认领 引用 被引量：1
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作者 Wenshuang Liang Beibei Gao +2 位作者 Lu Gao Wei Ge Fu Wang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第4期47-62,共16页
Lightfield microscopy(LFM),with its snapshot-based three-dimensional imaging capability,has become a vital tool for observing dynamic living specimens.To overcome limitations in resolution and reconstruction speed inh... Lightfield microscopy(LFM),with its snapshot-based three-dimensional imaging capability,has become a vital tool for observing dynamic living specimens.To overcome limitations in resolution and reconstruction speed inherent in traditional algorithms,the application of deep learning to lightfield microscopic imaging has emerged as a key development direction.This review will introduce the basic theory and classical algorithms of LFM,survey the application of deep learning-based methods in computational image enhancement and analysis,and discuss the associated challenges and future research directions. 展开更多
关键词 Lightfield deep learning reconstruction algorithm computational imaging
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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images 认领 引用 被引量：3
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作者 Sonali Das Saroja Kumar Rout +5 位作者 Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期893-916,共24页
In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects adults.Treatment depends on the type of leukemia... In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects adults.Treatment depends on the type of leukemia and the extent to which cancer has established throughout the body.Identifying leukemia in the initial stage is vital to providing timely patient care.Medical image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive processes.It can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human error.Many researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its subgroups.This study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical Images.The projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical images.The MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature extraction.Lastly,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia cancer.The hyperparameter tuning process using MPA helps enhance leukemia cancer classification performance.Simulation results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches. 展开更多
关键词 Leukemia cancer medical imaging image classification deep learning marine predators algorithm
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Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoT-Enhanced Smart Cities 认领 引用 被引量：2
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作者 Jing Zhang Xin Qi +1 位作者 San Hlaing Myint Zheng Wen 《Computers, Materials & Continua》 SCIE EI 2021年第8期2807-2824,共18页
With increasingly more smart cameras deployed in infrastructure and commercial buildings,3D reconstruction can quickly obtain cities’information and improve the efficiency of government services.Images collected in o... With increasingly more smart cameras deployed in infrastructure and commercial buildings,3D reconstruction can quickly obtain cities’information and improve the efficiency of government services.Images collected in outdoor hazy environments are prone to color distortion and low contrast;thus,the desired visual effect cannot be achieved and the difficulty of target detection is increased.Artificial intelligence(AI)solutions provide great help for dehazy images,which can automatically identify patterns or monitor the environment.Therefore,we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning.First,we propose a fine transmission image deep convolutional regression network(FT-DCRN)dehazing algorithm that uses fine transmission image and atmospheric light value to compute dehazed image.The DCRN is used to obtain the coarse transmission image,which can not only expand the receptive field of the network but also retain the features to maintain the nonlinearity of the overall network.The fine transmission image is obtained by refining the coarse transmission image using a guided filter.The atmospheric light value is estimated according to the position and brightness of the pixels in the original hazy image.Second,we use the dehazed images generated by the FT-DCRN dehazing algorithm for 3D reconstruction.An advanced relaxed iterative fine matching based on the structure from motion(ARI-SFM)algorithm is proposed.The ARISFM algorithm,which obtains the fine matching corner pairs and reduces the number of iterations,establishes an accurate one-to-one matching corner relationship.The experimental results show that our FT-DCRN dehazing algorithm improves the accuracy compared to other representative algorithms.In addition,the ARI-SFM algorithm guarantees the precision and improves the efficiency. 展开更多
关键词 3D reconstruction dehazed image deep learning fine transmission image structure from motion algorithm
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A fully automated quantitative analysis method based on deep learning algorithms for immunohistochemical staining expression intensities 认领 引用
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作者 Yongjian Deng Bojun Cai Xiaomei Wang 《Intelligent Oncology》 2025年第3期256-264,共9页
This paper focuses primarily on exploring the application of deep learning techniques and image processing algorithms in immunohistochemistry analysis,specifically targeting automated quantitative methods for nu-clear... This paper focuses primarily on exploring the application of deep learning techniques and image processing algorithms in immunohistochemistry analysis,specifically targeting automated quantitative methods for nu-clear,membrane,and cytoplasmic expressions of animal cells in whole-slide images.Cell nuclei,membranes,and cytoplasm were precisely identified and quantified by employing optical density separation techniques to differentiate between hematoxylin and 3,3'-diaminobenzidine staining components in combination with the CellViT nuclear segmentation algorithm and the region growing algorithm.Experimental validation demon-strates that the proposed algorithm performs excellently in terms of accuracy and recall.Compared to traditional manual interpretation,this algorithm achieve greater accuracy in specific quantitative metrics. 展开更多
关键词 Deep learning Immunohistochemistry analysis Image processing algorithm Optical density separation Quantification of whole-slide images
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Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral Image Classification 认领 引用
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作者 B.Chellapraba D.Manohari +1 位作者 K.Periyakaruppan M.S.Kavitha 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期2353-2366,共14页
Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is importa... Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is important for effective classification outcomes.Additionally,the recent advancements of deep learning(DL)models make it possible in several application areas.In addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics.In this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)technique.The proposed RDADL-HIC technique aims to effectively determine the HSI images.In addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimi-zer.Moreover,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of HSIs.The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively.The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures.The comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches. 展开更多
关键词 Hyperspectral images image classification deep learning adagrad optimizer nasnetlarge model red deer algorithm
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Current status of deep learning in abdominal image reconstruction 认领 引用
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作者 Guang-Yuan Li Cheng-Yan Wang Jun Lv 《Artificial Intelligence in Medical Imaging》 2021年第4期86-94,共9页
Abdominal magnetic resonance imaging(MRI)and computed tomography(CT)are commonly used for disease screening,diagnosis,and treatment guidance.However,abdominal MRI has disadvantages including slow speed and vulnerabili... Abdominal magnetic resonance imaging(MRI)and computed tomography(CT)are commonly used for disease screening,diagnosis,and treatment guidance.However,abdominal MRI has disadvantages including slow speed and vulnerability to motions,while CT suffers from problems of radiation.It has been reported that deep learning reconstruction can solve such problems while maintaining good image quality.Recently,deep learning-based image reconstruction has become a hot topic in the field of medical imaging.This study reviews the latest research on deep learning reconstruction in abdominal imaging,including the widely used convolutional neural network,generative adversarial network,and recurrent neural network. 展开更多
关键词 Abdominal imaging Reconstruction Magnetic resonance imaging Computed tomography Deep learning
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Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm 认领 引用 被引量：5
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作者 Shao-Jun Zhu Hao-Dong Zhan +4 位作者 Mao-Nian Wu Bo Zheng Bang-Quan Liu Shao-Chong Zhang Wei-Hua Yang 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2023年第7期995-1004,共10页
AIM:To conduct a classification study of high myopic maculopathy(HMM)using limited datasets,including tessellated fundus,diffuse chorioretinal atrophy,patchy chorioretinal atrophy,and macular atrophy,and minimize anno... AIM:To conduct a classification study of high myopic maculopathy(HMM)using limited datasets,including tessellated fundus,diffuse chorioretinal atrophy,patchy chorioretinal atrophy,and macular atrophy,and minimize annotation costs,and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification.METHODS:The optimized ALFA-Mix algorithm(ALFAMix+)was compared with five algorithms,including ALFA-Mix.Four models,including Res Net18,were established.Each algorithm was combined with four models for experiments on the HMM dataset.Each experiment consisted of 20 active learning rounds,with 100 images selected per round.The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+outperformed other algorithms.Finally,this study employed six models,including Efficient Former,to classify HMM.The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+algorithm to achieve satisfactor y classification results with a small dataset.RESULTS:ALFA-Mix+outperforms other algorithms with an average superiority of 16.6,14.75,16.8,and 16.7 rounds in terms of accuracy,sensitivity,specificity,and Kappa value,respectively.This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images.The Efficient Former achieved the best results with an accuracy,sensitivity,specificity,and Kappa value of 0.8821,0.8334,0.9693,and 0.8339,respectively.Therefore,by combining ALFA-Mix+with Efficient Former,this study achieved results with an accuracy,sensitivity,specificity,and Kappa value of 0.8964,0.8643,0.9721,and 0.8537,respectively.CONCLUSION:The ALFA-Mix+algorithm reduces the required samples without compromising accuracy.Compared to other algorithms,ALFA-Mix+outperforms in more rounds of experiments.It effectively selects valuable samples compared to other algorithms.In HMM classification,combining ALFA-Mix+with Efficient Former enhances model performance,further demonstrating the effectiveness of ALFA-Mix+. 展开更多
关键词 high myopic maculopathy deep learning active learning image classification ALFA-Mix algorithm
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Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm 认领 引用 被引量：3
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作者 Wei Ba Shuhao Wang +3 位作者 Cancheng Liu Yuefeng Wang Huaiyin Shi Zhigang Song 《Chinese Medical Sciences Journal》 CAS CSCD 2021年第3期204-209,共6页
Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images(WSIs).Methods We retrospectively collected 1,250 gastric biopsy ... Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images(WSIs).Methods We retrospectively collected 1,250 gastric biopsy specimens(1,128 gastritis,122 normal mucosa)from PLA General Hospital.The deep learning algorithm based on DeepLab v3(ResNet-50)architecture was trained and validated using 1,008 WSIs and 100 WSIs,respectively.The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs,with the pathologists’consensus diagnosis as the gold standard.Results The receiver operating characteristic(ROC)curves were generated for chronic superficial gastritis(CSuG),chronic active gastritis(CAcG),and chronic atrophic gastritis(CAtG)in the test set,respectively.The areas under the ROC curves(AUCs)of the algorithm for CSuG,CAcG,and CAtG were 0.882,0.905 and 0.910,respectively.The sensitivity and specificity of the deep learning algorithm for the classification of CSuG,CAcG,and CAtG were 0.790 and 1.000(accuracy 0.880),0.985 and 0.829(accuracy 0.901),0.952 and 0.992(accuracy 0.986),respectively.The overall predicted accuracy for three different types of gastritis was 0.867.By flagging the suspicious regions identified by the algorithm in WSI,a more transparent and interpretable diagnosis can be generated.Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs.By pre-highlighting the different gastritis regions,it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists. 展开更多
关键词 artificial intelligence deep learning algorithm gastritis whole-slide pathological images
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A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI:Development and Verification over Asia 认领 引用 被引量：1
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作者 Disong Fu Hongrong Shi +9 位作者 Christian AGueymard Dazhi Yang Yu Zheng Huizheng Che Xuehua Fan Xinlei Han Lin Gao Jianchun Bian Minzheng Duan Xiangao Xia 《Engineering》 SCIE EI CAS CSCD 2024年第7期164-174,共11页
The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral b... The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands,enabling the detection of highly variable aerosol optical depth(AOD).Quantitative retrieval of AOD has hitherto been challenging,especially over land.In this study,an AOD retrieval algorithm is proposed that combines deep learning and transfer learning.The algorithm uses core concepts from both the Dark Target(DT)and Deep Blue(DB)algorithms to select features for the machinelearning(ML)algorithm,allowing for AOD retrieval at 550 nm over both dark and bright surfaces.The algorithm consists of two steps:①A baseline deep neural network(DNN)with skip connections is developed using 10 min Advanced Himawari Imager(AHI)AODs as the target variable,and②sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters.Out-of-station validation shows that the retrieved AOD attains high accuracy,characterized by a coefficient of determination(R2)of 0.70,a mean bias error(MBE)of 0.03,and a percentage of data within the expected error(EE)of 70.7%.A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm,as well as the surface reflectance at 650 nm,are the two largest sources of uncertainty impacting the retrieval.In a case study of monitoring an extreme aerosol event,the AGRI AOD is found to be able to capture the detailed temporal evolution of the event.This work demonstrates the superiority of the transfer-learning technique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events. 展开更多
关键词 Aerosol optical depth Retrieval algorithm Deep learning Transfer learning Advanced Geosynchronous Radiation Imager
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Advancing automated pupillometry:a practical deep learning model utilizing infrared pupil images 认领 引用 被引量：1
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作者 Dai Guangzheng Yu Sile +2 位作者 Liu Ziming Yan Hairu He Xingru 《国际眼科杂志》 CAS 2024年第10期1522-1528,共7页
AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hos... AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included,and 13470 infrared pupil images were collected for the study.All infrared images for pupil segmentation were labeled using the Labelme software.The computation of pupil diameter is divided into four steps:image pre-processing,pupil identification and localization,pupil segmentation,and diameter calculation.Two major models are used in the computation process:the modified YoloV3 and Deeplabv 3+models,which must be trained beforehand.RESULTS:The test dataset included 1348 infrared pupil images.On the test dataset,the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils.The DeeplabV3+model achieved a background intersection over union(IOU)of 99.23%,a pupil IOU of 93.81%,and a mean IOU of 96.52%.The pupil diameters in the test dataset ranged from 20 to 56 pixels,with a mean of 36.06±6.85 pixels.The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels,with a mean absolute error(MAE)of 1.06±0.96 pixels.CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm,proven to be highly accurate and reliable for clinical application. 展开更多
关键词 pupil infrared image algorithm deep learning model
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Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images 认领 引用
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作者 Mesfer Al Duhayyim Hadeel Alsolai +5 位作者 Siwar Ben Haj Hassine Jaber SAlzahrani Ahmed SSalama Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani 《Computers, Materials & Continua》 SCIE EI 2023年第2期3167-3181,共15页
Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent ... Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images.Hyperspectral remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic spectrum.In order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and quantitativemonitoring.Particularly,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on vegetation.With this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing Images.The presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images.To accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature vectors.Besides,Elman neural network(ENN)model is applied to allot proper class labels into the input HSI.Finally,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this article.The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects.Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%. 展开更多
关键词 Hyperspectral images remote sensing deep learning hurricane optimization algorithm crop classification parameter tuning
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High speed ghost imaging based on a heuristic algorithm and deep learning 认领 引用
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作者 Yi-Yi Huang Chen Ou-Yang +4 位作者 Ke Fang Yu-Feng Dong Jie Zhang Li-Ming Chen Ling-An Wu 《Chinese Physics B》 SCIE EI CAS CSCD 2021年第6期287-293,共7页
We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new conden... We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well. 展开更多
关键词 high speed computational ghost imaging heuristic algorithm deep learning
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Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motioncompensated reconstruction, biomechanical modeling and deep learning 认领 引用
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作者 You Zhang Xiaokun Huang Jing Wang 《Visual Computing for Industry,Biomedicine,and Art》 EI 2019年第1期221-235,共15页
4-Dimensional cone-beam computed tomography(4D-CBCT)offers several key advantages over conventional 3DCBCT in moving target localization/delineation,structure de-blurring,target motion tracking,treatment dose accumula... 4-Dimensional cone-beam computed tomography(4D-CBCT)offers several key advantages over conventional 3DCBCT in moving target localization/delineation,structure de-blurring,target motion tracking,treatment dose accumulation and adaptive radiation therapy.However,the use of the 4D-CBCT in current radiation therapy practices has been limited,mostly due to its sub-optimal image quality from limited angular sampling of conebeam projections.In this study,we summarized the recent developments of 4D-CBCT reconstruction techniques for image quality improvement,and introduced our developments of a new 4D-CBCT reconstruction technique which features simultaneous motion estimation and image reconstruction(SMEIR).Based on the original SMEIR scheme,biomechanical modeling-guided SMEIR(SMEIR-Bio)was introduced to further improve the reconstruction accuracy of fine details in lung 4D-CBCTs.To improve the efficiency of reconstruction,we recently developed a U-net-based deformation-vector-field(DVF)optimization technique to leverage a population-based deep learning scheme to improve the accuracy of intra-lung DVFs(SMEIR-Unet),without explicit biomechanical modeling.Details of each of the SMEIR,SMEIR-Bio and SMEIR-Unet techniques were included in this study,along with the corresponding results comparing the reconstruction accuracy in terms of CBCT images and the DVFs.We also discussed the application prospects of the SMEIR-type techniques in image-guided radiation therapy and adaptive radiation therapy,and presented potential schemes on future developments to achieve faster and more accurate 4D-CBCT imaging. 展开更多
关键词 Cone-beam computed tomography Image reconstruction Motion estimation Biomechanical modeling Deep learning
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Deep learning-enhanced NIR-II fluorescence volumetric microscopy for dynamic 3D vascular imaging 认领 引用
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作者 Shiyi Peng Yuhuang Zhang +3 位作者 Xuanjie Mou Tianxiang Wu Mingxi Zhang Jun Qian 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2025年第3期154-164,共11页
Three-dimensional (3D) visualization of dynamic biological processes in deep tissue remains challenging due to the trade-off between temporal resolution and imaging depth. Here, we present a novel near-infrared-II (NI... Three-dimensional (3D) visualization of dynamic biological processes in deep tissue remains challenging due to the trade-off between temporal resolution and imaging depth. Here, we present a novel near-infrared-II (NIR-II, 900–1880nm) fluorescence volumetric microscopic imaging method that combines an electrically tunable lens (ETL) with deep learning approaches for rapid 3D imaging. The technology achieves volumetric imaging at 4.2 frames per second (fps) across a 200 μm depth range in live mouse brain vasculature. Two specialized neural networks are utilized: a scale-recurrent network (SRN) for image enhancement and a cerebral vessel interpolation (CVI) network that enables 16-fold axial upsampling. The SRN, trained on two-photon fluorescence microscopic data, improves both lateral and axial resolution of NIR-II fluorescence wide-field microscopic images. The CVI network, adapted from video interpolation techniques, generates intermediate frames between acquired axial planes, resulting in smooth and continuous 3D vessel reconstructions. Using this integrated system, we visualize and quantify blood flow dynamics in individual vessels and are capable of measuring blood velocity at different depths. This approach maintains high lateral resolution while achieving rapid volumetric imaging, and is particularly suitable for studying dynamic vascular processes in deep tissue. Our method demonstrates the potential of combining optical engineering with artificial intelligence to advance biological imaging capabilities. 展开更多
关键词 NIR-II fluorescence bioimaging volumetric microscopy deep learning reconstruction dynamic vascular imaging
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Precise Image算法在肺部CT成像中的应用价值 认领 引用 被引量：1
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作者 江宜桓 刘晓静 +5 位作者 宁先英 廖甜 马金强 吴红英 雷子乔 桂绅 《CT理论与应用研究（中英文）》 2026年第3期546-554,共9页
目的:比较人工智能深度学习Precise Image算法、滤波反投影重建算法(FBP)在肺部CT成像的图像质量,探讨Precise Image算法在肺部CT成像中的应用价值。方法:回顾性收集53例肺部CT扫描的病例,分别使用Precise Image和FBP的方法重建平扫图... 目的:比较人工智能深度学习Precise Image算法、滤波反投影重建算法(FBP)在肺部CT成像的图像质量,探讨Precise Image算法在肺部CT成像中的应用价值。方法:回顾性收集53例肺部CT扫描的病例,分别使用Precise Image和FBP的方法重建平扫图像。其中Precise Image算法采用低(sharp)、中(stand)、高(smooth)3种重建方式。提取两种不同重建算法肺窗和软组织窗图像:lung和med。由两位放射医师分别对肺窗4组、软组织窗2组图像进行评分,采集各组图像数据,使用SPSS对评分结果与获得数据进行统计学处理。结果:肺窗4组图像,SD值、CNR和SNR差异均具有统计学意义。软组织窗2组图像,CT值、SD值、CNR和SNR差异均具有统计学意义。肺窗中,lung-smooth组的SD值最低,SNR和CNR最高;lung-FBP组的SD值最高,SNR和CNR最低。软组织窗中,med-stand组SD值明显低于med-FBP组。主观评价显示,图像质量最佳是med-stand组,病变显示和诊断信心最佳是lung-stand组。主观评分最低是lung-FBP组。结论:与FBP相比,Precise Image重建图像能够更有效的降低图像噪声、减少图像伪影,能够得到较好的图像质量;在选择的3个Precise Image重建权重得到的图像中,stand组主观评分相较其他重建权重更高。 展开更多
关键词 CT成像 深度学习 图像重建 Precise Image算法 肺部
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Deep learning tomographic reconstruction through hierarchical decomposition of domain transforms 认领 引用 被引量：1
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作者 Lin Fu Bruno De Man 《Visual Computing for Industry,Biomedicine,and Art》 EI 2022年第1期365-377,共13页
Deep learning(DL)has shown unprecedented performance for many image analysis and image enhancement tasks.Yet,solving large-scale inverse problems like tomographic reconstruction remains challenging for DL.These proble... Deep learning(DL)has shown unprecedented performance for many image analysis and image enhancement tasks.Yet,solving large-scale inverse problems like tomographic reconstruction remains challenging for DL.These problems involve non-local and space-variant integral transforms between the input and output domains,for which no efficient neural network models are readily available.A prior attempt to solve tomographic reconstruction problems with supervised learning relied on a brute-force fully connected network and only allowed reconstruction with a 1284system matrix size.This cannot practically scale to realistic data sizes such as 5124and 5126for three-dimensional datasets.Here we present a novel framework to solve such problems with DL by casting the original problem as a continuum of intermediate representations between the input and output domains.The original problem is broken down into a sequence of simpler transformations that can be well mapped onto an efficient hierarchical network architecture,with exponentially fewer parameters than a fully connected network would need.We applied the approach to computed tomography(CT)image reconstruction for a 5124 system matrix size.This work introduces a new kind of data-driven DL solver for full-size CT reconstruction without relying on the structure of direct(analytical)or iterative(numerical)inversion techniques.This work presents a feasibility demonstration of full-scale learnt reconstruction,whereas more developments will be needed to demonstrate superiority relative to traditional reconstruction approaches.The proposed approach is also extendable to other imaging problems such as emission and magnetic resonance reconstruction.More broadly,hierarchical DL opens the door to a new class of solvers for general inverse problems,which could potentially lead to improved signal-to-noise ratio,spatial resolution and computational efficiency in various areas. 展开更多
关键词 Computed tomography Image reconstruction Deep learning Hierarchical
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