FRC-QE: A robust and comparable 3D microscopy image quality metric for cleared organoids.
Ontology highlight
ABSTRACT: Summary
Here, we propose Fourier ring correlation-based quality estimation (FRC-QE) as a new metric for automated image quality estimation in 3 D fluorescence microscopy acquisitions of cleared organoids that yields comparable measurements across experimental replicates, clearing protocols and works for different microscopy modalities.Availability and implementation
FRC-QE is written in ImgLib2/Java and provided as an easy-to-use and macro-scriptable plugin in Fiji. Code, documentation, sample images and further information can be found under https://github.com/PreibischLab/FRC-QE.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Preusser F
PROVIDER: S-EPMC8479654 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:We have investigated the effect of Airy illumination on the image quality and depth penetration of digitally scanned light-sheet microscopy in turbid neural tissue. We used Fourier analysis of images acquired using Gaussian and Airy light-sheets to assess their respective image quality versus penetration into the tissue. We observed a three-fold average improvement in image quality at 50 μm depth with the Airy light-sheet. We also used optical clearing to tune the scattering properties of the tissue and found that the improvement when using an Airy light-sheet is greater in the presence of stronger sample-induced aberrations. Finally, we used homogeneous resolution probes in these tissues to quantify absolute depth penetration in cleared samples with each beam type. The Airy light-sheet method extended depth penetration by 30% compared to a Gaussian light-sheet.
| S-EPMC5102539 | biostudies-literature
Project description:The combination of optical tissue transparency with immunofluorescence allows the molecular characterization of biological tissues in 3D. However, adult human organs are particularly challenging to become transparent because of the autofluorescence contributions of aged tissues. To meet this challenge, we optimized SHORT (SWITCH-H2O2-antigen Retrieval-TDE), a procedure based on standard histological treatments in combination with a refined clearing procedure to clear and label portions of the human brain. 3D histological characterization with multiple molecules is performed on cleared samples with a combination of multi-colors and multi-rounds labeling. By performing fast 3D imaging of the samples with a custom-made inverted light-sheet fluorescence microscope (LSFM), we reveal fine details of intact human brain slabs at subcellular resolution. Overall, we proposed a scalable and versatile technology that in combination with LSFM allows mapping the cellular and molecular architecture of the human brain, paving the way to reconstruct the entire organ.
| S-EPMC9098858 | biostudies-literature
Project description:A modern color filter array (CFA) output is rendered into the final output image using a demosaicing algorithm. During this process, the rendered image is affected by optical and carrier cross talk of the CFA pattern and demosaicing algorithm. Although many CFA patterns have been proposed thus far, an image-quality (IQ) evaluation system capable of comprehensively evaluating the IQ of each CFA pattern has yet to be developed, although IQ evaluation items using local characteristics or specific domain have been created. Hence, we present an IQ metric system to evaluate the IQ performance of CFA patterns. The proposed CFA evaluation system includes proposed metrics such as the moiré robustness using the experimentally determined moiré starting point (MSP) and achromatic reproduction (AR) error, as well as existing metrics such as color accuracy using CIELAB, a color reproduction error using spatial CIELAB, structural information using the structure similarity, the image contrast based on MTF50, structural and color distortion using the mean deviation similarity index (MDSI), and perceptual similarity using Haar wavelet-based perceptual similarity index (HaarPSI). Through our experiment, we confirmed that the proposed CFA evaluation system can assess the IQ for an existing CFA. Moreover, the proposed system can be used to design or evaluate new CFAs by automatically checking the individual performance for the metrics used.
| S-EPMC7213733 | biostudies-literature
Project description:Automated analysis of microscope images is necessitated by the increased need for high-resolution follow up of events in time. Manually finding the right images to be analyzed, or eliminated from data analysis are common day-to-day problems in microscopy research today, and the constantly growing size of image datasets does not help the matter. We propose a simple method and a software tool for sorting images within a dataset, according to their relative quality. We demonstrate the applicability of our method in finding good quality images in a STED microscope sample preparation optimization image dataset. The results are validated by comparisons to subjective opinion scores, as well as five state-of-the-art blind image quality assessment methods. We also show how our method can be applied to eliminate useless out-of-focus images in a High-Content-Screening experiment. We further evaluate the ability of our image quality ranking method to detect out-of-focus images, by extensive simulations, and by comparing its performance against previously published, well-established microscopy autofocus metrics.
| S-EPMC4929473 | biostudies-literature
Project description:Automated fundus screening is becoming a significant programme of telemedicine in ophthalmology. Instant quality evaluation of uploaded retinal images could decrease unreliable diagnosis. In this work, we propose fractal dimension of retinal vasculature as an easy, effective and explainable indicator of retinal image quality. The pipeline of our approach is as follows: utilize image pre-processing technique to standardize input retinal images from possibly different sources to a uniform style; then, an improved deep learning empowered vessel segmentation model is employed to extract retinal vessels from the pre-processed images; finally, a box counting module is used to measure the fractal dimension of segmented vessel images. A small fractal threshold (could be a value between 1.45 and 1.50) indicates insufficient image quality. Our approach has been validated on 30,644 images from four public database.
| S-EPMC9279448 | biostudies-literature
Project description:High-throughput sectioning and optical imaging of tissue samples using traditional immunohistochemical techniques can be costly and inaccessible in resource-limited areas. We demonstrate three-dimensional (3D) imaging and phenotyping in optically transparent tissue using lens-free holographic on-chip microscopy as a low-cost, simple, and high-throughput alternative to conventional approaches. The tissue sample is passively cleared using a simplified CLARITY method and stained using 3,3'-diaminobenzidine to target cells of interest, enabling bright-field optical imaging and 3D sectioning of thick samples. The lens-free computational microscope uses pixel super-resolution and multi-height phase recovery algorithms to digitally refocus throughout the cleared tissue and obtain a 3D stack of complex-valued images of the sample, containing both phase and amplitude information. We optimized the tissue-clearing and imaging system by finding the optimal illumination wavelength, tissue thickness, sample preparation parameters, and the number of heights of the lens-free image acquisition and implemented a sparsity-based denoising algorithm to maximize the imaging volume and minimize the amount of the acquired data while also preserving the contrast-to-noise ratio of the reconstructed images. As a proof of concept, we achieved 3D imaging of neurons in a 200-μm-thick cleared mouse brain tissue over a wide field of view of 20.5 mm2. The lens-free microscope also achieved more than an order-of-magnitude reduction in raw data compared to a conventional scanning optical microscope imaging the same sample volume. Being low cost, simple, high-throughput, and data-efficient, we believe that this CLARITY-enabled computational tissue imaging technique could find numerous applications in biomedical diagnosis and research in low-resource settings.
| S-EPMC5553818 | biostudies-other
Project description:BackgroundPathological analyses and methodology has recently undergone a dramatic revolution. With the creation of tissue clearing methods such as CLARITY and CUBIC, groups can now achieve complete transparency in tissue samples in nano-porous hydrogels. Cleared tissue is then imagined in a semi-aqueous medium that matches the refractive index of the objective being used. However, one major challenge is the ability to control tissue movement during imaging and to relocate precise locations post sequential clearing and re-staining.MethodsUsing 3D printers, we designed tissue molds that fit precisely around the specimen being imaged. First, images are taken of the specimen, followed by importing and design of a structural mold, then printed with affordable plastics by a 3D printer.ResultsWith our novel design, we have innovated tissue molds called innovative molds (iMolds) that can be generated in any laboratory and are customized for any organ, tissue, or bone matter being imaged. Furthermore, the inexpensive and reusable tissue molds are made compatible for any microscope such as single and multi-photon confocal with varying stage dimensions. Excitingly, iMolds can also be generated to hold multiple organs in one mold, making reconstruction and imaging much easier.ConclusionsTaken together, with iMolds it is now possible to image cleared tissue in clearing medium while limiting movement and being able to relocate precise anatomical and cellular locations on sequential imaging events in any basic laboratory. This system provides great potential for screening widespread effects of therapeutics and disease across entire organ systems.
| S-EPMC5497344 | biostudies-literature
Project description:Fluorescence microscopy is an essential tool for biological discoveries. There is a constant demand for better spatial resolution across a larger field of view. Although strides have been made to improve the theoretical resolution and speed of the optical instruments, in mesoscopic samples, image quality is still largely limited by the optical properties of the sample. In Selective Plane Illumination Microscopy (SPIM), the achievable optical performance is hampered by optical degradations encountered in both the illumination and detection. Multi-view imaging, either through sample rotation or additional optical paths, is a popular strategy to improve sample coverage. In this work, we introduce a smart rotation workflow that utilizes on-the-fly image analysis to identify the optimal light sheet imaging orientations. The smart rotation workflow outperforms the conventional approach without additional hardware and achieves a better sample coverage using the same number of angles or less and thereby reduces data volume and phototoxicity.
| S-EPMC6952408 | biostudies-literature
Project description:Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation. Recent deep learning approaches learn deformation models directly from data. However, they provide limited control over the spatial regularity of transformations. Instead of learning the entire registration approach, we learn a spatially-adaptive regularizer within a registration model. This allows controlling the desired level of regularity and preserving structural properties of a registration model. For example, diffeomorphic transformations can be attained. Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself. Source code is publicly-available at https://github.com/uncbiag/registration.
| S-EPMC7286567 | biostudies-literature
Project description:We provide here a procedure enabling light sheet fluorescence microscopy (LSFM) of entire human eyes after iDISCO + -based clearing (ClearEye) and immunolabeling. Demonstrated here in four eyes, post-processing of LSFM stacks enables three-dimensional (3D) navigation and customized display, including en face viewing of the fundus similarly to clinical imaging, with resolution of retinal capillaries. This method overcomes several limitations of traditional histology of the eyes. Tracing of spatially complex structures such as anterior ciliary vessels and Schlemm's canal was achieved. We conclude that LSFM of immunolabeled human eyes after iDISCO + -based clearing is a powerful tool for 3D histology of large human ocular samples, including entire eyes, which will be useful in both anatomopathology and in research.
| S-EPMC10564773 | biostudies-literature