Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV最新文献_第2页

Microsphere-aided imaging of subdiffraction-limited translucent features (Conference Presentation) 微球辅助成像的亚衍射限制半透明特征(会议报告)

Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV Pub Date : 2019-07-22 DOI: 10.1117/12.2526725

S. Perrin, P. Montgomery, S. Lecler

引用次数: 0

Secondary speckle-based tomography and tissue probing (Conference Presentation) 基于斑点的二次断层扫描和组织探测(会议报告)

Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV Pub Date : 2019-07-22 DOI: 10.1117/12.2524968

A. Schwarz, N. Ozana, R. Califa, A. Shemer, H. Genish, Z. Zalevsky

{"title":"Secondary speckle-based tomography and tissue probing (Conference Presentation)","authors":"A. Schwarz, N. Ozana, R. Califa, A. Shemer, H. Genish, Z. Zalevsky","doi":"10.1117/12.2524968","DOIUrl":"https://doi.org/10.1117/12.2524968","url":null,"abstract":"We will present how one can use the spatial-temporal analysis of secondary speckle patterns that are generated when laser light is back scattered from a tissue in order to measure the nano-vibrations (tilting associated vibrations) occurring in the tissue and in order to map its elastography. In addition to the fundamental nano-vibrations sensing capability, the proposed configuration allows by applying time multiplexing approach also to perform separation of photons coming from different depths of the tissue while externally stimulating the tissue via infra-sonic vibration. This yields a tomographic capability. The proposed configuration uses a modulated laser that allows combining a speckle pattern tracking method for surface tilting changes sensing with a Mach–Zehnder interferometer-based speckle patterns configuration to achieve z-axis detection (movement of the whole surface in the z direction). We will also show several methods for setup modulation to down convert high temporal frequencies to allow their sampling with a slow rate camera. As to be demonstrated in the experimental validation, the different elastographic layers (that were represented in our experiments by different concentrations of the agarose) have different temporal flickering and thereafter different temporal-spectral distribution which allows to extract their different elastographic characters.","PeriodicalId":308921,"journal":{"name":"Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121748236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Video rate scanning endomicroscopy through a coherent fiber bundle using a galvo scanner (Conference Presentation) 利用频动扫描仪通过相干光纤束进行视频速率扫描显微内镜(会议报告)

Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV Pub Date : 2019-07-22 DOI: 10.1117/12.2526054

E. Scharf, R. Kuschmierz, J. Czarske

引用次数: 0

Digital holography in optogenetics: a new window to the brain (Conference Presentation) 光遗传学中的数字全息术:通向大脑的新窗口(会议报告)

Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV Pub Date : 2019-07-22 DOI: 10.1117/12.2526503

J. Czarske

引用次数: 0

Automated cell identification with 3D optical imaging 自动细胞识别与三维光学成像

Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV Pub Date : 2019-06-21 DOI: 10.1117/12.2527573

B. Javidi, T. O’Connor, A. Anand, I. Moon, A. Markman

{"title":"Automated cell identification with 3D optical imaging","authors":"B. Javidi, T. O’Connor, A. Anand, I. Moon, A. Markman","doi":"10.1117/12.2527573","DOIUrl":"https://doi.org/10.1117/12.2527573","url":null,"abstract":"In this keynote address paper, we overview recently published works on the current techniques and methods for automated cell identification with 3D optical imaging using compact and field portable systems. 3D imaging systems including digital holographic microscopy systems as well as lensless pseudorandom phase encoding systems are capable of capturing 3D information of microscopic objects such as biological cells which allows for highly accurate automated cell identification. Systems based on digital holography enable reconstruction of the cell’s 3D optical path length profile. The reconstructed 3D profiles can be used to extract morphological and spatio-temporal cell features from biological samples for classification and cell identification. Similarly, pseudorandom encoding techniques such as single random phase encoding (SRPE) and double random phase encoding (DRPE) can be used to encode 3D cell information into opto-biological signatures which can be used for cell identification tasks. Recent advancements in these areas are presented including compact and field-portable 3D-printed shearing digital holographic microscopy systems, integration of digital holographic microscopy with head mounted augmented reality devices, and the use of spatio-temporal features extracted from cell membrane fluctuations for sickle cell disease diagnosis.","PeriodicalId":308921,"journal":{"name":"Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126736527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Toward a thinking microscope: deep learning-enabled computational microscopy and sensing (Conference Presentation) 走向思考显微镜:支持深度学习的计算显微镜和传感(会议报告)

Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV Pub Date : 2019-03-04 DOI: 10.1117/12.2524937

A. Ozcan

{"title":"Toward a thinking microscope: deep learning-enabled computational microscopy and sensing (Conference Presentation)","authors":"A. Ozcan","doi":"10.1117/12.2524937","DOIUrl":"https://doi.org/10.1117/12.2524937","url":null,"abstract":"Deep learning is a class of machine learning techniques that uses multi-layered artificial neural networks for automated analysis of signals or data. The name comes from the general structure of deep neural networks, which consist of several layers of artificial neurons, each performing a nonlinear operation, stacked over each other. Beyond its main stream applications such as the recognition and labeling of specific features in images, deep learning holds numerous opportunities for revolutionizing image formation, reconstruction and sensing fields. In fact, deep learning is mysteriously powerful and has been surprising optics researchers in what it can achieve for advancing optical microscopy, and introducing new image reconstruction and transformation methods. From physics-inspired optical designs and devices, we are moving toward data-driven designs that will holistically change both optical hardware and software of next generation microscopy and sensing, blending the two in new ways. Today, we sample an image and then act on it using a computer. Powered by deep learning, next generation optical microscopes and sensors will understand a scene or an object and accordingly decide on how and what to sample based on a given task – this will require a perfect marriage of deep learning with new optical microscopy hardware that is designed based on data. For such a thinking microscope, unsupervised learning would be the key to scale up its impact on various areas of science and engineering, where access to labeled image data might not be immediately available or very costly, difficult to acquire. In this presentation, I will provide an overview of some of our recent work on the use of deep neural networks in advancing computational microscopy and sensing systems, also covering their biomedical applications.","PeriodicalId":308921,"journal":{"name":"Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124734606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0