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Zhang L, Xu F, Neri F. An Asynchronous Spiking Neural Membrane System for Edge Detection. Int J Neural Syst 2024; 34:2450023. [PMID: 38490956 DOI: 10.1142/s0129065724500230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Spiking neural membrane systems (SN P systems) are a class of bio-inspired models inspired by the activities and connectivity of neurons. Extensive studies have been made on SN P systems with synchronization-based communication, while further efforts are needed for the systems with rhythm-based communication. In this work, we design an asynchronous SN P system with resonant connections where all the enabled neurons in the same group connected by resonant connections should instantly produce spikes with the same rhythm. In the designed system, each of the three modules implements one type of the three operations associated with the edge detection of digital images, and they collaborate each other through the resonant connections. An algorithm called EDSNP for edge detection is proposed to simulate the working of the designed asynchronous SN P system. A quantitative analysis of EDSNP and the related methods for edge detection had been conducted to evaluate the performance of EDSNP. The performance of the EDSNP in processing the testing images is superior to the compared methods, based on the quantitative metrics of accuracy, error rate, mean square error, peak signal-to-noise ratio and true positive rate. The results indicate the potential of the temporal firing and the proper neuronal connections in the SN P system to achieve good performance in edge detection.
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Affiliation(s)
- Luping Zhang
- Jiangxi Engineering Technology Research Center of Nuclear, Geoscience Data Science and System, Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology, School of Information Engineering, East China University of Technology, Nanchang 330013, Jiangxi, P. R. China
| | - Fei Xu
- Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Ferrante Neri
- NICE Research Group, School of Computer Science and Electronic Engineering, University of Surrey, Guildford, Surrey GU2 7XH, UK
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Bayhaqi YA, Hamidi A, Navarini AA, Cattin PC, Canbaz F, Zam A. Real-time closed-loop tissue-specific laser osteotomy using deep-learning-assisted optical coherence tomography. BIOMEDICAL OPTICS EXPRESS 2023; 14:2986-3002. [PMID: 37342720 PMCID: PMC10278623 DOI: 10.1364/boe.486660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 05/15/2023] [Accepted: 05/18/2023] [Indexed: 06/23/2023]
Abstract
This article presents a real-time noninvasive method for detecting bone and bone marrow in laser osteotomy. This is the first optical coherence tomography (OCT) implementation as an online feedback system for laser osteotomy. A deep-learning model has been trained to identify tissue types during laser ablation with a test accuracy of 96.28 %. For the hole ablation experiments, the average maximum depth of perforation and volume loss was 0.216 mm and 0.077 mm3, respectively. The contactless nature of OCT with the reported performance shows that it is becoming more feasible to utilize it as a real-time feedback system for laser osteotomy.
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Affiliation(s)
- Yakub. A. Bayhaqi
- Biomedical Laser and Optics Group (BLOG), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
| | - Arsham Hamidi
- Biomedical Laser and Optics Group (BLOG), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
| | - Alexander A. Navarini
- Digital Dermatology Group, Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
| | - Philippe C. Cattin
- Center for medical Image Analysis and Navigation (CIAN), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
| | - Ferda Canbaz
- Biomedical Laser and Optics Group (BLOG), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
| | - Azhar Zam
- Biomedical Laser and Optics Group (BLOG), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
- Division of Engineering, New York University Abu Dhabi, Abu Dhabi, 129188, United Arab Emirates
- Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA
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Bayhaqi YA, Hamidi A, Canbaz F, Navarini AA, Cattin PC, Zam A. Deep-Learning-Based Fast Optical Coherence Tomography (OCT) Image Denoising for Smart Laser Osteotomy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2615-2628. [PMID: 35442883 DOI: 10.1109/tmi.2022.3168793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence Tomography (OCT) to monitor the ablation process toward our smart laser osteotomy approach. The OCT image is helpful to identify tissue type and provide feedback for the ablation laser to avoid critical tissues such as bone marrow and nerve. Furthermore, in the implementation, the tissue classifier's accuracy is dependent on the quality of the OCT image. Therefore, image denoising plays an important role in having an accurate feedback system. A common OCT image denoising technique is the frame-averaging method. Inherent to this method is the need for multiple images, i.e., the more images used, the better the resulting image quality. However, this approach comes at the price of increased acquisition time and sensitivity to motion artifacts. To overcome these limitations, we applied a deep-learning denoising method capable of imitating the frame-averaging method. The resulting image had a similar image quality to the frame-averaging and was better than the classical digital filtering methods. We also evaluated if this method affects the tissue classifier model's accuracy that will provide feedback to the ablation laser. We found that image denoising significantly increased the accuracy of the tissue classifier. Furthermore, we observed that the classifier trained using the deep learning denoised images achieved similar accuracy to the classifier trained using frame-averaged images. The results suggest the possibility of using the deep learning method as a pre-processing step for real-time tissue classification in smart laser osteotomy.
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HyCAD-OCT: A Hybrid Computer-Aided Diagnosis of Retinopathy by Optical Coherence Tomography Integrating Machine Learning and Feature Maps Localization. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10144716] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Optical Coherence Tomography (OCT) imaging has major advantages in effectively identifying the presence of various ocular pathologies and detecting a wide range of macular diseases. OCT examinations can aid in the detection of many retina disorders in early stages that could not be detected in traditional retina images. In this paper, a new hybrid computer-aided OCT diagnostic system (HyCAD) is proposed for classification of Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV) and drusen disorders, while separating them from Normal OCT images. The proposed HyCAD hybrid learning system integrates the segmentation of Region of Interest (RoI), based on central serious chorioretinopathy (CSC) in Spectral Domain Optical Coherence Tomography (SD-OCT) images, with deep learning architectures for effective diagnosis of retinal disorders. The proposed system assimilates a range of techniques including RoI localization and feature extraction, followed by classification and diagnosis. An efficient feature fusion phase has been introduced for combining the OCT image features, extracted by Deep Convolutional Neural Network (CNN), with the features extracted from the RoI segmentation phase. This fused feature set is used to predict multiclass OCT retina disorders. The proposed segmentation phase of retinal RoI regions adds substantial contribution as it draws attention to the most significant areas that are candidate for diagnosis. A new modified deep learning architecture (Norm-VGG16) is introduced integrating a kernel regularizer. Norm-VGG16 is trained from scratch on a large benchmark dataset and used in RoI localization and segmentation. Various experiments have been carried out to illustrate the performance of the proposed system. Large Dataset of Labeled Optical Coherence Tomography (OCT) v3 benchmark is used to validate the efficiency of the model compared with others in literature. The experimental results show that the proposed model achieves relatively high-performance in terms of accuracy, sensitivity and specificity. An average accuracy, sensitivity and specificity of 98.8%, 99.4% and 98.2% is achieved, respectively. The remarkable performance achieved reflects that the fusion phase can effectively improve the identification ratio of the urgent patients’ diagnostic images and clinical data. In addition, an outstanding performance is achieved compared to others in literature.
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Ognard J, Mesrar J, Benhoumich Y, Misery L, Burdin V, Ben Salem D. Edge detector-based automatic segmentation of the skin layers and application to moisturization in high-resolution 3 Tesla magnetic resonance imaging. Skin Res Technol 2019; 25:339-346. [PMID: 30657209 DOI: 10.1111/srt.12654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Accepted: 12/09/2018] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Previous studies have demonstrated the feasibility to explore moisturization with quantification imaging based on T2 mapping. The aim of this study was to describe and validate the first robust automated method to segment the first layers of the skin. MATERIALS AND METHODS Data were picked from a previous study that included 35 healthy subjects who underwent a 3T MRI (multi spin echo calculation T2-weighted sequence) with a microscopic coil on the left heel before and one hour after moisturization. The automatic algorithm was composed of the T2 map generation, a Canny filter, a selection of boundaries, and a local regression to delimitate stratum corneum, epidermis, and dermis. An automated affine registration was applied between the exams before and after moisturization. RESULTS The failure rate of the algorithm was below 5%. Mean computation time was 139.12s. There was a significant and strong correlation between the automatic measurements and the manual ones for the T2 values (ρ: 0.905, P < 0.001) and for the thickness measurements (ρ: 0.8663; P < 0.001). For registration, mean of the Dice index was 0.64 [0.47; 0.80] and of the Hausdorff distance was 0.29 mm 95% CI: [0.28; 0.30]. CONCLUSION The proposed automatic method to study the first skin layers in 3T MRI using micro-coils was robust and described T2 values and thickness measurements with a strong correlation to manual measurements. The use of an automated affine registration could also permit the generation of a mapping for a visual assessment of moisturization.
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Affiliation(s)
- Julien Ognard
- Department of Radiology, University Hospital of Brest, Brest Cedex, France.,Laboratory of medical information processing - LaTIM INSERM UMR 1101, Brest Cedex, France
| | - Jawad Mesrar
- Department of Radiology, University Hospital of Brest, Brest Cedex, France.,Laboratory of medical information processing - LaTIM INSERM UMR 1101, Brest Cedex, France
| | - Younes Benhoumich
- Laboratory of medical information processing - LaTIM INSERM UMR 1101, Brest Cedex, France
| | - Laurent Misery
- Department of Dermatology, University Hospital of Brest, Brest, France.,Laboratory of Epithelium Neurons Interactions - LIEN EA4685, Brest Cedex, France
| | - Valerie Burdin
- Laboratory of medical information processing - LaTIM INSERM UMR 1101, Brest Cedex, France.,Mines-Telecom Institute - IMT Atlantique, Plouzané, France
| | - Douraied Ben Salem
- Department of Radiology, University Hospital of Brest, Brest Cedex, France
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Allen WM, Kennedy KM, Fang Q, Chin L, Curatolo A, Watts L, Zilkens R, Chin SL, Dessauvagie BF, Latham B, Saunders CM, Kennedy BF. Wide-field quantitative micro-elastography of human breast tissue. BIOMEDICAL OPTICS EXPRESS 2018; 9:1082-1096. [PMID: 29541505 PMCID: PMC5846515 DOI: 10.1364/boe.9.001082] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 01/30/2018] [Accepted: 01/31/2018] [Indexed: 05/18/2023]
Abstract
Currently, 20-30% of patients undergoing breast-conserving surgery require a second surgery due to insufficient surgical margins in the initial procedure. We have developed a wide-field quantitative micro-elastography system for the assessment of tumor margins. In this technique, we map tissue elasticity over a field-of-view of ~46 × 46 mm. We performed wide-field quantitative micro-elastography on thirteen specimens of freshly excised tissue acquired from patients undergoing a mastectomy. We present wide-field optical coherence tomography (OCT) images, qualitative (strain) micro-elastograms and quantitative (elasticity) micro-elastograms, acquired in 10 minutes. We demonstrate that wide-field quantitative micro-elastography can extend the range of tumors visible using OCT-based elastography by providing contrast not present in either OCT or qualitative micro-elastography and, in addition, can reduce imaging artifacts caused by a lack of contact between tissue and the imaging window. Also, we describe how the combined evaluation of OCT, qualitative micro-elastograms and quantitative micro-elastograms can improve the visualization of tumor.
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Affiliation(s)
- Wes M. Allen
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, Western Australia, 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, 35 Stirling Highway, Perth, Western Australia, 6009, Australia
| | - Kelsey M. Kennedy
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, Western Australia, 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, 35 Stirling Highway, Perth, Western Australia, 6009, Australia
| | - Qi Fang
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, Western Australia, 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, 35 Stirling Highway, Perth, Western Australia, 6009, Australia
| | - Lixin Chin
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, Western Australia, 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, 35 Stirling Highway, Perth, Western Australia, 6009, Australia
| | - Andrea Curatolo
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, Western Australia, 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, 35 Stirling Highway, Perth, Western Australia, 6009, Australia
| | - Lucinda Watts
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, Western Australia, 6009, Australia
- School of Surgery, The University of Western Australia, 35 Stirling Highway, Perth, Western Australia, 6009, Australia
| | - Renate Zilkens
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, Western Australia, 6009, Australia
- School of Surgery, The University of Western Australia, 35 Stirling Highway, Perth, Western Australia, 6009, Australia
| | - Synn Lynn Chin
- Breast Centre, Fiona Stanley Hospital, 11 Robin Warren Drive, Murdoch, Western Australia, 6150, Australia
| | - Benjamin F. Dessauvagie
- PathWest, Fiona Stanley Hospital, 11 Robin Warren Drive, Murdoch, Western Australia, 6150, Australia
- School of Pathology and Laboratory Medicine, The University of Western Australia, 35 Stirling Highway, Perth, Western Australia, 6009, Australia
| | - Bruce Latham
- PathWest, Fiona Stanley Hospital, 11 Robin Warren Drive, Murdoch, Western Australia, 6150, Australia
| | - Christobel M. Saunders
- School of Surgery, The University of Western Australia, 35 Stirling Highway, Perth, Western Australia, 6009, Australia
- Breast Centre, Fiona Stanley Hospital, 11 Robin Warren Drive, Murdoch, Western Australia, 6150, Australia
- Breast Clinic, Royal Perth Hospital, 197 Wellington Street, Perth, Western Australia, 6000, Australia
| | - Brendan F. Kennedy
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research, The University of Western Australia, Perth, Western Australia, 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, 35 Stirling Highway, Perth, Western Australia, 6009, Australia
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