Single molecule localization microscopy is widely used in biological research for measuring the nanostructures of samples smaller than the diffraction limit. This study uses multifocal plane microscopy and addresses the 3D single molecule localization problem, where lateral and axial locations of molecules are estimated. However, when we multifocal plane microscopy is used, the estimation accuracy of 3D localization is easily deteriorated by the small lateral drifts of camera positions. We formulate a 3D molecule localization problem along with the estimation of the lateral drifts as a compressed sensing problem, A deep neural network was applied to accurately and efficiently solve this problem. The proposed method is robust to the lateral drifts and achieves an accuracy of 20 nm laterally and 50 nm axially without an explicit drift correction.
Saudi Electronic University（沙特电子大学）；The Commonwealth Scientific and Industrial Research Organisation（澳大利亚联邦科学与工业研究组织）；The Australian National University（澳大利亚国立大学）；University of Technology Sydney（悉尼科技大学）；the University of Western Australia（西澳大利亚大学）
Accurate localization of proteins from fluorescence microscopy images is challenging due to the inter-class similarities and intra-class disparities introducing grave concerns in addressing multi-class classification problems. Conventional machine learning-based image prediction pipelines rely heavily on pre-processing such as normalization and segmentation followed by hand-crafted feature extraction to identify useful, informative, and application-specific features. Here, we demonstrate that deep learning-based pipelines can effectively classify protein images from different datasets. We prop…
New York University（纽约大学）；University Hospital Bonn（波恩大学医院）
Object counting and localization are key steps for quantitative analysis in large-scale microscopy applications. This procedure becomes challenging when target objects are overlapping, are densely clustered, and/or present fuzzy boundaries. Previous methods producing density maps based on deep learning have reached a high level of accuracy for object counting by assuming that object counting is equivalent to the integration of the density map. However, this model fails when objects show significant overlap regarding accurate localization. We propose an alternative method to count and localize …
Eindhoven University of Technology（埃因霍温理工大学）；Academic Medical Center, University of Amsterdam（阿姆斯特丹大学学术医学中心）；The University of Washington（华盛顿大学）
Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents (microbubbles) across numerous imaging frames. However, analysis of high-density regions with significant overlaps among the microbubble point spread responses yields high localization errors, constraining the technique to low-concentration conditions. As such, long acquisition times are required to sufficiently cover the vascular bed. In this work, we present a fast and precise method for obtaining super-resolution vascular images from high-den…
Light-field microscopes are able to capture spatial and angular information of incident light rays. This allows reconstructing 3D locations of neurons from a single snap-shot.In this work, we propose a model-inspired deep learning approach to perform fast and robust 3D localization of sources using light-field microscopy images. This is achieved by developing a deep network that efficiently solves a convolutional sparse coding (CSC) problem to map Epipolar Plane Images (EPI) to corresponding sparse codes. The network architecture is designed systematically by unrolling the convolutional Iterat…
Technion - Israel Institute of Technology（以色列理工学院）；Google Research（谷歌研究院）
A long-standing challenge in multiple-particle-tracking is the accurate and precise 3D localization of individual particles at close proximity. One established approach for snapshot 3D imaging is point-spread-function (PSF) engineering, in which the PSF is modified to encode the axial information. However, engineered PSFs are challenging to localize at high densities due to lateral PSF overlaps. Here we suggest using multiple PSFs simultaneously to help overcome this challenge, and investigate the problem of engineering multiple PSFs for dense 3D localization. We implement our approach using a…
Vector Institute for AI Research（向量人工智能研究所）；University of Toronto（多伦多大学）
Protein function is inherently linked to its localization within the cell, and fluorescent microscopy data is an indispensable resource for learning representations of proteins. Despite major developments in molecular representation learning, extracting functional information from biological images remains a non-trivial computational task. Current state-of-the-art approaches use autoencoder models to learn high-quality features by reconstructing images. However, such methods are prone to capturing noise and imaging artifacts. In this work, we revisit deep learning models used for classifying m…
The Institute of Cancer Research（癌症研究所）；University of Warwick（华威大学）；University Hospitals Coventry and Warwickshire（考文垂与华威郡大学医院）；The Alan Turing Institute（艾伦·图灵研究所）
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. The network trains at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution decon…
A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy
Polytechnique Montréal（蒙特利尔理工学院）；Institute of Biomedical Engineering, Polytechnique Montréal（蒙特利尔理工学院生物医学工程研究所）；Mila（米拉研究所）；Institute of Cardiology of Montréal（蒙特利尔心脏病研究所）
University of Alberta（阿尔伯塔大学）；Cross Cancer Institute（十字癌症研究所）
Automated cell detection and localization from microscopy images are significant tasks in biomedical research and clinical practice. In this paper, we design a new cell detection and localization algorithm that combines deep convolutional neural network (CNN) and compressed sensing (CS) or sparse coding (SC) for end-to-end training. We also derive, for the first time, a backpropagation rule, which is applicable to train any algorithm that implements a sparse code recovery layer. The key observation behind our algorithm is that cell detection task is a point object detection task in computer vi…
King’s College London（伦敦国王学院）；Helmholtz Centre for Infection Research（亥姆霍兹感染研究中心）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Snap, Inc.（Snap公司）；Randall Centre for Cell & Molecular Biophysics, King’s College London（伦敦国王学院兰德尔细胞与分子生物物理学中心）
Understanding the structure of a protein complex is crucial indetermining its function. However, retrieving accurate 3D structures from microscopy images is highly challenging, particularly as many imaging modalities are two-dimensional. Recent advances in Artificial Intelligence have been applied to this problem, primarily using voxel based approaches to analyse sets of electron microscopy images. Herewe present a deep learning solution for reconstructing the protein com-plexes from a number of 2D single molecule localization microscopy images, with the solution being completely unconstrained…
Artefact-Aware Fungal Detection in Dermatophytosis: A Real-Time Transformer-Based Approach for KOH Microscopy
Yildiz Technical University（伊兹密尔技术大学）；Imperial College London（帝国理工学院伦敦分校）；Istanbul Research and Training Hospital（伊斯坦布尔研究与培训医院）；Medicana Atakoy Hospital（梅迪卡纳阿塔科伊医院）
