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Biophotonics Congress: Optics in the Life Sciences 2023 (OMA, NTM, BODA, OMP, BRAIN)最新文献

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An Integrated Platform for Multi-Algorithm Fluorescence Fluctuation Super-Resolution Nanoscopy 多算法荧光波动超分辨率纳米显微镜集成平台
Wenbo Li, Z. Zeng
An integrated platform for multi-algorithm fluorescence fluctuation super-resolution nanoscopy is developed. The platform integrates 4 super-resolution algorithms for image reconstructions. Subcellular details can be discerned at the resolution beyond the diffraction limit using this platform.
开发了一种多算法荧光涨落超分辨纳米显微镜集成平台。该平台集成了4种超分辨率图像重建算法。使用该平台可以在超过衍射极限的分辨率下识别亚细胞细节。
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引用次数: 0
Optical Coherence Tomography Enabled Classification of the Human Venoatrial Junction 光学相干断层扫描对人静脉心房连接处的分类
A. Joasil, Aidan M. Therien, C. Hendon
The venoatrial junction is an important substrate of atrial fibrillation. We showcase that OCT sub-images of the venoatrial junction can be classified as left atrium or pulmonary vein using a deep CNN with high accuracy.
静脉心房交界是心房颤动的重要底物。我们展示了使用深度CNN可以高精度地将静脉房交界处的OCT亚图像分类为左心房或肺静脉。
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引用次数: 0
Digital Labeling of the Vasculature: Toward Label-Free and Labor-Free Vessel Segmentation 血管系统的数字标记:迈向无标签和无人工的血管分割
Maryse Lapierre-Landry, Yehe Liu, Mahdi Bayat, D. L. Wilson, Michael W. Jenkins
We propose digital labeling, a method for automated, three-dimensional segmentation of blood vessels without vascular contrast agents. Our deep learning approach greatly simplifies the sample preparation required for 3D microscopy and accelerate image post-processing.
我们提出了数字标签,一种不需要血管造影剂的血管自动三维分割方法。我们的深度学习方法大大简化了3D显微镜所需的样品制备,并加速了图像后处理。
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引用次数: 0
Breaking new ground in protein detection: Self-supervised machine learning and iSCAT enable label-free detection of single proteins below 10 kDa 在蛋白质检测方面开辟新天地:自我监督机器学习和iSCAT能够对低于10 kDa的单个蛋白质进行无标签检测
Mahyar Dahmardeh, Houman Mirzaalian Dastjerdi, Hisham Mazal, Harald Köstler, V. Sandoghdar
Interferometric scattering (iSCAT) microscopy detects single nanoparticles in a label-free fashion. Utilizing self-supervised machine learning pushes the detection sensitivity of iSCAT to very small proteins and disease markers such as chemokines and cytokines.
干涉散射(iSCAT)显微镜以无标签的方式检测单个纳米颗粒。利用自我监督的机器学习提高了iSCAT对非常小的蛋白质和疾病标志物(如趋化因子和细胞因子)的检测灵敏度。
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引用次数: 0
In Vivo Imaging of Neuronal Activity and Blood Vessels in AAV-Injected Mice 注射aav小鼠神经元活动和血管的体内成像
Anaïs Parrot, Jérémie Guilbert, Pierre Girard-Collins, Michèle Desjardins
Characterizing neurovascular coupling helps to understand neurodegenerative diseases. Here, neuronal activity and vasculature were simultaneously measured in AAV-injected mice through transcranial windows by two-photon microscopy indicating strongly correlated signals synchronized with whiskers stimulations.
表征神经血管耦合有助于理解神经退行性疾病。在这里,通过双光子显微镜通过经颅窗同时测量了aav注射小鼠的神经元活动和血管系统,表明与须刺激同步的强相关信号。
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引用次数: 0
Machine Learning-based Analysis of ICG-Assisted NIR Spectral Data for Diagnosing Pancreatic Carcinoma 基于机器学习的icg辅助近红外光谱数据诊断胰腺癌
Orna Mukhopadhyay
Near-infrared imaging (NIR) combined with machine learning can provide a high-throughput procedure for detecting tumors with high sensitivity and specificity, We present an end-to-end machine learning-based framework for fast, accurate diagnosis of pancreatic carcinoma.
近红外成像(NIR)结合机器学习可以提供一种高通量的方法来检测肿瘤,具有高灵敏度和特异性。我们提出了一个基于端到端机器学习的框架,用于快速、准确地诊断胰腺癌。
{"title":"Machine Learning-based Analysis of ICG-Assisted NIR Spectral Data for Diagnosing Pancreatic Carcinoma","authors":"Orna Mukhopadhyay","doi":"10.1364/ntm.2023.ntu1c.7","DOIUrl":"https://doi.org/10.1364/ntm.2023.ntu1c.7","url":null,"abstract":"Near-infrared imaging (NIR) combined with machine learning can provide a high-throughput procedure for detecting tumors with high sensitivity and specificity, We present an end-to-end machine learning-based framework for fast, accurate diagnosis of pancreatic carcinoma.","PeriodicalId":111173,"journal":{"name":"Biophotonics Congress: Optics in the Life Sciences 2023 (OMA, NTM, BODA, OMP, BRAIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125839328","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
Deep Learning Driven Light-Based Motor Mapping of Multiple Limb Movements in Mice Reveals Behaviorally Relevant Movements 深度学习驱动的基于光的小鼠多肢体运动映射揭示了行为相关的运动
Pub Date : 1900-01-01 DOI: 10.1364/brain.2023.btu1b.5
Nischal Khanal, Jonah A. Padawer-Curry, Kevin Schulte, T. Voss, A. Bice, Jin-Moo Lee, A. Bauer
We demonstrate a hybrid method utilizing DeepLabCut and LBMM to concurrently localize motor representations of multiple limbs in mice. Results suggest that motor movements involving multiple limbs reside in overlapping cortical representations of each limb.
我们展示了一种利用DeepLabCut和LBMM同时定位小鼠多肢运动表征的混合方法。结果表明,涉及多个肢体的运动存在于每个肢体重叠的皮层表征中。
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引用次数: 0
Deep Learning Methods for Sensorless Adaptive Optics 无传感器自适应光学的深度学习方法
Guozheng Xu, Isabelle Garnreiter, Thomas J. Smart, Michael G. Chambers, Eduard Durech, Ringo Ng, Jennifer Sun, M. Sarunic
We present our progress on image-guided (wavefront sensor-less) adaptive optics using Deep Learning Methods to optimize the image quality using high numerical aperture OCT and confocal microscopy with a custom developed instrument.
我们介绍了我们在图像引导(无波前传感器)自适应光学方面的进展,使用深度学习方法优化图像质量,使用高数值孔径OCT和共聚焦显微镜与定制开发的仪器。
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引用次数: 0
Optical tweezers with integrated multiplane microscopy 光学镊子集成多平面显微镜
L. Paterson, A. Matheson, A. Wright, Tania Mendonca, M. Tassieri, P. Dalgarno
Optical tweezer have been combined with multiplane microscopy to measure particle trajectories in three dimensions and to calculate the viscosity of the suspending media in three dimensions at micrometer length scales. The method is applicable to biological samples.
光学镊子与多平面显微镜相结合,测量了三维粒子的运动轨迹,并在微米长度尺度上计算了三维悬浮介质的粘度。该方法适用于生物样品。
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引用次数: 0
Revealing the ultra-structure of microorganisms using tabletop extreme ultraviolet ptychography 利用桌面极紫外照相技术揭示微生物的超微结构
C. Liu, W. Eschen, L. Loetgering, D. Molina, R. Klas, A. Iliou, M. Steinert, S. Herkersdorf, A. Kirsche, T. Pertsch, F. Hillmann, J. Limpert, J. Rothhardt
We report on our latest biological imaging results on a tabletop ptychographic microscope at 13.5 nm wavelength. Retrieved amplitude and phase images with sub-60-nm resolution enable the identification of the nanoscale material composition inside microorganisms.
我们报告了我们最新的生物成像结果在桌上型显微镜在13.5纳米波长。提取的振幅和相位图像具有低于60纳米的分辨率,可以识别微生物内部的纳米级物质组成。
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引用次数: 0
期刊
Biophotonics Congress: Optics in the Life Sciences 2023 (OMA, NTM, BODA, OMP, BRAIN)
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