PySight: plug and play photon counting for fast continuous volumetric intravital microscopy

Har-Gil, Hagai; Golgher, Lior; Israel, Shai; Kain, David; Cheshnovsky, Ori; Parnas, Moshe; Blinder, Pablo

doi:10.1364/optica.5.001104

Cited by 16 publications

(21 citation statements)

References 48 publications

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“…To minimize motion artifacts during imaging, its head was restrained to a custom-made holder and its platform was clamped to the imaging stage. We examine two datasets acquired using rapid volumetric two-photon laser scanning microscopy [14]. The first dataset ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation

et al. 2019

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Recently developed methods for rapid continuous volumetric two-photon microscopy facilitate the observation of neuronal activity in hundreds of individual neurons and changes in blood flow in adjacent blood vessels across a large volume of living brain at unprecedented spatio-temporal resolution. However, the high imaging rate necessitates fully automated image analysis, whereas tissue turbidity and photo-toxicity limitations lead to extremely sparse and noisy imagery. In this work, we extend a recently proposed deep learning volumetric blood vessel segmentation network, such that it supports temporal analysis. With this technology, we are able to track changes in cerebral blood volume over time and identify spontaneous arterial dilations that propagate towards the pial surface. This new capability is a promising step towards characterizing the hemodynamic response function upon which functional magnetic resonance imaging (fMRI) is based.

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Section: Resultsmentioning

confidence: 99%

Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation

et al. 2019

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“…One key aspect when implementing the Lissajous microscope is synchronizing the scanned excitation with the detection. This task is typically accomplished with fast acquisition cards [15,16]. However, the relative high frequency of the TAG lens requires electronics significantly faster than those commonly employed [16].…”

Section: As Shown Inmentioning

confidence: 99%

“…This task is typically accomplished with fast acquisition cards [15,16]. However, the relative high frequency of the TAG lens requires electronics significantly faster than those commonly employed [16]. To ease implementation, we designed a three-step approach that obviates the need of fast electronics, as shown in Figure 1d.…”

Section: As Shown Inmentioning

confidence: 99%

Volumetric Lissajous Confocal Microscopy

Deguchi

Bianchini

Palazzolo

et al. 2019

Preprint

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Dynamic biological systems present challenges to existing three-dimensional (3D) optical microscopes because of their continuous temporal and spatial changes. Most techniques are based on rigid architectures, as in confocal microscopy, where a laser beam is sequentially scanned at a predefined spatial sampling rate and pixel dwell time. Here, we developed volumetric Lissajous confocal microscopy to achieve unsurpassed 3D scanning speed with a tunable sampling rate. The system combines an acoustic liquid lens for continuous axial focus translation with a resonant scanning mirror. Accordingly, the excitation beam follows a dynamic Lissajous trajectory enabling sub-millisecond acquisitions of image series containing 3D information at a sub-Nyquist sampling rate. By temporal accumulation and/or advanced interpolation algorithms, volumetric imaging rate is selectable using a post-processing step at the desired spatiotemporal resolution for events of interest. We demonstrate multicolor and calcium imaging over volumes of tens of cubic microns with acquisition speeds up to 5 kHz.

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“…The field of microscopic imaging of blood vessels is evolving rapidly. For example, several emerging techniques allow rapid volumetric imaging of vascular dynamics in optically clear [5] and turbid [22,14] living brains. Such technologies can lead to breakthroughs, both in understanding neuro-vascular interactions in the neocortex and in finegrained medical diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…These limitations include: (i) the datasets are limited in size, due to the amount of expert labor required, (ii) the datasets do not represent the very high variability that exists in imaging conditions between microscopes, between imaged samples, and along the same experiment. (iii) High-throughput imaging modalities [5,14,22] necessitate faster segmentation algorithms that do not rely on human curation.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network

Gur

Wolf

Golgher

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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The task of blood vessel segmentation in microscopy images is crucial for many diagnostic and research applications. However, vessels can look vastly different, depending on the transient imaging conditions, and collecting data for supervised training is laborious.We present a novel deep learning method for unsupervised segmentation of blood vessels. The method is inspired by the field of active contours and we introduce a new loss term, which is based on the morphological Active Contours Without Edges (ACWE) optimization method. The role of the morphological operators is played by novel pooling layers that are incorporated to the network's architecture.We demonstrate the challenges that are faced by previous supervised learning solutions, when the imaging conditions shift. Our unsupervised method is able to outperform such previous methods in both the labeled dataset, and when applied to similar but different datasets. Our code, as well as efficient pytorch reimplementations of the baseline methods VesselNN and DeepVess is available on GitHub https://github.com/shirgur/UMIS

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PySight: plug and play photon counting for fast continuous volumetric intravital microscopy

Cited by 16 publications

References 48 publications

Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation

Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation

Volumetric Lissajous Confocal Microscopy

Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network

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