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arXiv 2024年

作者： Liu, Shuhong Deng, Tianchen Zhou, Heng Li, Liuzhuozheng Wang, Hongyu Wang, Danwei Li, Mingrui Department of Information Science and Technology Department of Complexity Science and Engineering The University of Tokyo Tokyo113-8654 Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Mechanical Engineering Columbia University New York10027 United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Dalian University of Technology Dalian116024 China

Gaussian Splatting SLAMs have made significant advancements in improving the efficiency and fidelity of real-time reconstructions. However, these systems often encounter incomplete reconstructions in complex indoor environments, characterized by substantial holes due to unobserved geometry caused by obstacles or limited view angles. To address this challenge, we present Manhattan Gaussian SLAM, an RGB-D system that leverages the Manhattan World hypothesis to enhance geometric accuracy and completeness. By seamlessly integrating fused line segments derived from structured scenes, our method ensures robust tracking in textureless indoor areas. Moreover, The extracted lines and planar surface assumption allow strategic interpolation of new Gaussians in regions of missing geometry, enabling efficient scene completion. Extensive experiments conducted on both synthetic and real-world scenes demonstrate that these advancements enable our method to achieve state-of-the-art performance, marking a substantial improvement in the capabilities of Gaussian SLAM systems. © 2024, CC BY.

关键词： Gaussian distribution

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, Computer Retrospective Study

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Mesenchymal stem cells loaded on 3D-printed gradient poly(e-caprolactone)/methacrylated alginate composite scaffolds for cartilage tissue engineering

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Regenerative Biomaterials 2021年第3期8卷 70-83页

作者： Yanyan Cao Peng Cheng Shengbo Sang Chuan Xiang Yang An Xiaochun Wei Zhizhong Shen Yixia Zhang Pengcui Li Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education MicroNano System Research CenterCollege of Information and ComputerTaiyuan University of TechnologyTaiyuan 030024China College of Information Science and Engineering Hebei North UniversityZhangjiakou 075000China Shanxi Key Laboratory of Bone and Soft Tissue Injury Repair Department of OrthopedicsThe Second Hospital of Shanxi Medical UniversityTaiyuan 030001China Department of Plastic Surgery Peking University Third HospitalBeijing 100191China Department of Biomedical Engineering College of Biomedical EngineeringTaiyuan University of TechnologyTaiyuan 030024China

Cartilage has limited self-repair ability due to its avascular,alymphatic and aneural *** combination of three-dimensional(3D)printing and tissue engineering provides an up-and-coming approach to address this ***,we designed and fabricated a tri-layered(superficial layer(SL),middle layer(ML)and deep layer(DL))stratified scaffold,inspired by the architecture of collagen fibers in native cartilage *** scaffold was composed of 3D printed depth-dependent gradient poly(e-caprolactone)(PCL)impregnated with methacrylated alginate(ALMA),and its morphological analysis and mechanical properties were *** prove the feasibility of the composite scaffolds for cartilage regeneration,the viability,proliferation,collagen deposition and chondrogenic differentiation of embedded rat bone marrow mesenchymal stem cells(BMSCs)in the scaffolds were assessed by Live/dead assay,CCK-8,DNA content,cell morphology,immunofluorescence and real-time reverse transcription polymerase chain ***-loaded gradient PCL/ALMA scaffolds showed excellent cell survival,cell proliferation,cell morphology,collagen II deposition and hopeful chondrogenic differentiation compared with three individual-layer ***,our study demonstrates the potential use of the gradient PCL/ALMA construct for enhanced cartilage tissue engineering.

关键词： three-dimensional printing cartilage tissue engineering PCL methacrylated alginate bone marrow mesenchymal stem cells

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Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features

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Genomics and informatics 2024年第1期22卷 1-10页

作者： Meng, Chaolu Hou, Yongqi Zou, Quan Shi, Lei Su, Xi Ju, Ying College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Hohhot China School of Computer Science Inner Mongolia University Hohhot China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China Department of Spine Surgery Changzheng Hospital Naval Medical University Huangpu District No. 415 Fengyang Road Shanghai China Foshan Women and Children Hospital Foshan China School of Informatics Xiamen University Xiamen China

In protein identification, researchers increasingly aim to achieve efficient classification using fewer features. While many feature selection methods effectively reduce the number of model features, they often cause information loss caused by merely selecting or discarding features, which limits classifier performance. To address this issue, we present Rore, an algorithm based on a feature-dimensionality reduction strategy. By mapping the original features to a latent space, Rore retains all relevant feature information while using fewer representations of the latent features. This approach significantly preserves the original information and overcomes the information loss problem associated with previous feature selection. Through extensive experimental validation and analysis, Rore demonstrated excellent performance on an antioxidant protein dataset, achieving an accuracy of 95.88% and MCC of 91.78%, using vectors including only 15 features. The Rore algorithm is available online at http://112.124.26.17:8021/Rore. © The Author(s) 2024.

关键词： Dimensionality reduction Protein classifier Protein sequence Robust feature

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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture

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Journal of Computer Science & technology 2022年第2期37卷 330-343页

作者： Xin Zhang Siyuan Lu Shui-Hua Wang Xiang Yu Su-Jing Wang Lun Yao Yi Pan Yu-Dong Zhang Department of Medical Imaging The Fourth People's Hospital of Huai'anHuai'an 223002China School of Informatics University of LeicesterLeicesterLE17RHU.K. School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUU.K. School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHU.K. Key Laboratory of Behavior Sciences Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of the Chinese Academy of SciencesBeijing 100101China Department of Infection Diseases The Fourth People's Hospital of Huai'anHuaian 223002China Department of Computer Science Georgia State UniversityAtlanta 30302-5060U.S.A. Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah 21589Saudi Arabia

COVID-19 is a contagious infection that has severe effects on the global economy and our daily *** diagnosis of COVID-19 is of importance for consultants,patients,and *** this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and ***,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,*** comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed *** addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

关键词： pneumonia COVID-19 convolutional neural network AlexNet deep learning

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Few-shot learning based histopathological image classification of colorectal cancer

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Intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

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An integrated rehabilitation system for the upper limb spasticity assessment and treatment: the Rehabotics passive exoskeletal system

An integrated rehabilitation system for the upper limb spast...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Vassiliki Potsika Nikolaos Tachos Athanasios Pardalis Christoforos Papaioannou Ioannis Kouris Theodore Economopoulos Pantelis Syringas Nikolaos Tselikas Orestis Zestas Giorgos Papagiannis Athanasios Triantafyllou Vasilis Protopappas Nikolaos Malizos George Matsopoulos Dimitrios I. Fotiadis Department of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina GR Greece Biomedical Engineering Laboratory National Technical University of Athens Athens Greece CNA Lab - Department of Informatics and Telecommunications University of Peloponnese Tripoli Greece Physiotherapy Department Biomechanics Laboratory University of the Peloponnese Sparta Greece and Physioloft Physiotherapy Center Kifisia Greece Ostracon Ltd Greece Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina GR Greece Biomedical Research Institute Foundation for Research and Technology-Hellas (FORTH)

Limb spasticity is caused by stroke, multiple sclerosis, traumatic brain injury and various central nervous system pathologies such as brain tumors resulting in joint stiffness, loss of hand function and severe pain. This paper presents with the Rehabotics integrated rehabilitation system aiming to provide highly individualized assessment and treatment of the function of the upper limbs for patients with spasticity after stroke, focusing on the developed passive exoskeletal system. The proposed system can: (i) measure various motor and kinematic parameters of the upper limb in order to evaluate the patient's condition and progress, as well as (ii) offer a specialized rehabilitation program (therapeutic exercises, retraining of functional movements and support of daily activities) through an interactive virtual environment. The outmost aim of this multidisciplinary research work is to create new tools for providing high-level treatment and support services to patients with spasticity after *** Relevance— This paper presents a new passive exoskeletal system aiming to provide enhanced treatment and assessment of patients with upper limb spasticity after stroke.

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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Computer Systems Science & engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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Detection and Selection of Moving Objects in Video Images Based on Impulse and Recurrent Neural Networks

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Journal of Data Analysis and information Processing 2022年第2期10卷 127-141页

作者： Ihar Yeuseyenka Ihar Melnikau Ihar Yemelyanov Research Center of Automotive Power Train and Electronic Control College of Automotive Engineering Hubei University of Automotive Technology Shiyan China Department of Asset Performance and Information Management AVEVA Solutions Ltd. Cambridge The United Kingdom Laboratory of Mathematical Cybernetics United Institute of Informatics Problems Minsk Belarus

The purpose of the article is to develop a methodology for automating the detection and selection of moving objects. The detection and separation of moving objects based on impulse and recurrence neural networks simulation. The result of the work is a developed motion detector based on impulse and recurrence neural networks and an automated system developed on the basis of this detector for detecting and separating moving objects and is ready for practical application. The feasibility of integrating the developed motion detector with Emgu CV (OpenCV) image processing package, multimedia framework functions, and DirectShow application programming interface were investigated. The proposed approach and software for the detection and separating of moving objects in video images using neural networks can be integrated into more sophisticated specialized computer-aided video surveillance systems, IoT (Internet of Things), IoV (Internet of Vehicles), etc.

关键词： Automated System Video Image Pixel Neuron Neural Network

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Procedure Recognition by Knowledge-Driven Segmentation in Robotic-Assisted Vitreoretinal Surgery

Procedure Recognition by Knowledge-Driven Segmentation in Ro...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhen Li Yawen Deng Qiang Ye Weihong Yu Haoxiang Qi Yaliang Liu Zhangguo Yu Gui-Bin Bian School of Electronic and Information Engineering Tongji University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China School of Mechatronic Engineering Beijing Institute of Technology Beijing China Department of Ophthalmology Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Key Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Internal limiting membrane (ILM) peeling is a vital vitreoretinal surgery procedure. However, due to the thickness of just 1-2 micrometers and the intricacies associated with its varying density and adhesion, the difficulty of manipulation exceeds the physiological limits of human perception and operation. Surgical robot is characterized by high precision and stability. However, navigating intricate intraocular environments and handling minuscule high-precision areas remain enormous challenges. These include issues of uneven lighting, field-of-view loss, and motion blur. This paper proposed a perception method named ‘Multimodal Surgical Process Recognition based on Domain Knowledge and Segmentation (MSPR-DKS),’ designed to address these challenges and provide input for the precise control of robots. Moreover, a comprehensive dataset focused on ILM peeling during macular hole surgeries was established. Experimental results underscore the efficacy of this approach, with segmentation accuracies exceeding 99.27% for instruments and macular holes and an average accuracy of 98.97% in recognizing surgical processes. This study paves the way for leveraging domain knowledge and image segmentation to improve robot-assisted manipulation of soft tissues in ophthalmology.

关键词： Image segmentation Visualization Accuracy medical robotics Motion segmentation Surgery Lighting

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