scholarly journals Robust and unbiased estimation of the background distribution for automated quantitative imaging

2021 ◽  
Author(s):  
Mauro Silberberg ◽  
Hernán Edgardo Grecco
Keyword(s):  
Quantitative Imaging ◽  
Fine Tuning ◽  
Unbiased Estimation ◽  
Background Distribution ◽  
Background Estimation ◽  
Robust Segmentation ◽  
Segmentation Methods ◽  
Additional Input ◽  
Averaging Filter ◽  
Automated Algorithms

Quantitative analysis of high-throughput microscopy images requires robust automated algorithms. Background estimation is usually the first step and has an impact on all subsequent analysis, in particular for foreground detection and calculation of ratiometric quantities. Most methods recover only a single background value, such as the median. Those that aim to retrieve a background distribution by dividing the intensity histogram yield a biased estimation in images in non-trivial cases. In this work, we present the first method to recover an unbiased estimation of the background distribution directly from an image and without any additional input. Through a robust statistical test, our method leverages the lack of local spatial correlation in background pixels to select a subset of pixels that accurately represent the background distribution. This method is both fast and simple to implement, as it only uses standard mathematical operations and an averaging filter. Additionally, the only parameter, the size of the averaging filter, does not require fine tuning. The obtained background distribution can be used to test for foreground membership of individual pixels, or to estimate confidence intervals in derived quantities. We expect that the concepts described in this work can help to develop a novel family of robust segmentation methods.

Robust segmentation methods with an application to aortic pulse wave velocity calculation

2014 ◽  
Vol 38 (3) ◽  
pp. 179-189 ◽  
Author(s):  
Danilo Babin ◽  
Daniel Devos ◽  
Aleksandra Pižurica ◽  
Jos Westenberg ◽  
Ewout Vansteenkiste ◽  
...  
Keyword(s):  
Pulse Wave Velocity ◽  
Wave Velocity ◽  
Pulse Wave ◽  
Aortic Pulse Wave Velocity ◽  
Robust Segmentation ◽  
Segmentation Methods

scholarly journals Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform

2017 ◽  
Vol 8 ◽  
pp. 2572-2582 ◽  
Author(s):  
Yuliang Wang ◽  
Tongda Lu ◽  
Xiaolai Li ◽  
Shuai Ren ◽  
Shusheng Bi
Keyword(s):  
Atomic Force Microscope ◽  
Hough Transform ◽  
Morphological Characterization ◽  
Automated Segmentation ◽  
Transform Method ◽  
Atomic Force ◽  
Robust Segmentation ◽  
Segmentation Methods ◽  
Boundary Optimization

Interfacial nanobubbles (NBs) and nanodroplets (NDs) have been attracting increasing attention due to their potential for numerous applications. As a result, the automated segmentation and morphological characterization of NBs and NDs in atomic force microscope (AFM) images is highly awaited. The current segmentation methods suffer from the uneven background in AFM images due to thermal drift and hysteresis of AFM scanners. In this study, a two-step approach was proposed to segment NBs and NDs in AFM images in an automated manner. The spherical Hough transform (SHT) and a boundary optimization operation were combined to achieve robust segmentation. The SHT was first used to preliminarily detect NBs and NDs. After that, the so-called contour expansion operation was applied to achieve optimized boundaries. The principle and the detailed procedure of the proposed method were presented, followed by the demonstration of the automated segmentation and morphological characterization. The result shows that the proposed method gives an improved segmentation result compared with the thresholding and circle Hough transform method. Moreover, the proposed method shows strong robustness of segmentation in AFM images with an uneven background.

scholarly journals Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tzu-Ching Wu ◽  
Xu Wang ◽  
Linlin Li ◽  
Ye Bu ◽  
David M. Umulis
Keyword(s):  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Image Data ◽  
True Positive Rate ◽  
Fine Tuning ◽  
Data Sets ◽  
Nuclei Segmentation ◽  
Robust Segmentation ◽  
Positive Rate ◽  
Segmentation Image

AbstractIdentification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio. The wavelet-based method achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to zebrafish embryonic development IN TOTO for nuclei segmentation, image registration, and nuclei shape analysis. These new approaches to segmentation provide a means to carry out quantitative patterning analysis with single-cell precision throughout three dimensional tissues and embryos and they have a high tolerance for non-uniform and noisy image data sets.

On the Design of Active Contours for Medical Image Segmentation

2003 ◽  
Vol 42 (01) ◽  
pp. 89-98 ◽  
Author(s):  
J. Bredno ◽  
K. Spitzer ◽  
T.M. Lehmann
Keyword(s):  
Medical Image ◽  
Object Representation ◽  
Active Contours ◽  
General Purpose ◽  
Medical Image Segmentation ◽  
Snake Model ◽  
Essential Information ◽  
Applied Model ◽  
Robust Segmentation ◽  
Segmentation Methods

Summary Objectives: To provide a comprehensive bottom-up categorization of model-based segmentation techniques that allows to select, implement, and apply well-suited active contour models for segmentation of medical images, where major challenges are the high variability in shape and appearance of objects, noise, artifacts, partial occlusions of objects, and the required reliability and correctness of results. Methods: We consider the general purpose of segmentation, the dimension of images, the object representation within the model, image and contour influences, as well as the solution and the parameter selection of the model. Potentials and limits are characterized for all instances in each category providing essential information for the application of active contours to various purposes in medical image processing. Based on prolaps surgery planning, we exemplify the use of the scheme to successfully design robust 3D-segmentation. Results: The construction scheme allows to design robust segmentation methods, which, in particular, should avoid any gaps of dimension. Such gaps result from different image domains and value ranges with respect to the applied model domain and the dimension of relevant subsets for image influences, respectively. Conclusions: A general segmentation procedure with sufficient robustness for medical applications is still missing. It is shown that in almost every category, novel techniques are available to improve the initial snake model, which was introduced in 1987.

scholarly journals WaveletSEG: Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification

2020 ◽  
Author(s):  
Tzu-Ching Wu ◽  
Xu Wang ◽  
Linlin Li ◽  
Ye Bu ◽  
David M. Umulis
Keyword(s):  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Image Data ◽  
True Positive Rate ◽  
Fine Tuning ◽  
Data Sets ◽  
Nuclei Segmentation ◽  
Robust Segmentation ◽  
Positive Rate ◽  
Segmentation Image

AbstractIdentification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio (SNR). The wavelet-based method achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to zebrafish embryonic development IN TOTO for nuclei segmentation, image registration, and nuclei shape analysis. These new approaches to segmentation provide a means to carry out quantitative patterning analysis with single-cell precision throughout three dimensional tissues and embryos and they have a high tolerance for non-uniform and noisy image data sets.

scholarly journals Fast and Robust Segmentation of Copy Number Profiles Using Multi-Scale Edge Detection

2016 ◽  
Author(s):  
Ivo W Kwee ◽  
Andrea Rinaldi ◽  
Cassio Polpo de Campos ◽  
Francesco Bertoni
Keyword(s):  
Edge Detection ◽  
Copy Number ◽  
State Of The Art ◽  
Copy Number Data ◽  
Multi Scale ◽  
Robust Segmentation ◽  
Segmentation Methods ◽  
Novel Method ◽  
Genomic Waves

ABSTRACTRaw copy number data is highly dimensional, noisy and can suffer from so-called genomic wave artifacts. We introduce a novel method based on multi-scale edge detection in derivative space. By using derivatives, the algorithm was very fast and robust against genomic waves. Our method compared very well to existing state-of-the-art segmentation methods and importantly outperformed these if noise and wave artifacts were well present.

scholarly journals Underwater image segmentation in the wild using deep learning

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Paulo Drews-Jr ◽  
Isadora de Souza ◽  
Igor P. Maurell ◽  
Eglen V. Protas ◽  
Silvia S. C. Botelho
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Simulated Data ◽  
Ground Truth ◽  
Fine Tuning ◽  
Training Images ◽  
Underwater Image ◽  
Segmentation Methods ◽  
In The Wild ◽  
Restoration Strategies

AbstractImage segmentation is an important step in many computer vision and image processing algorithms. It is often adopted in tasks such as object detection, classification, and tracking. The segmentation of underwater images is a challenging problem as the water and particles present in the water scatter and absorb the light rays. These effects make the application of traditional segmentation methods cumbersome. Besides that, to use the state-of-the-art segmentation methods to face this problem, which are based on deep learning, an underwater image segmentation dataset must be proposed. So, in this paper, we develop a dataset of real underwater images, and some other combinations using simulated data, to allow the training of two of the best deep learning segmentation architectures, aiming to deal with segmentation of underwater images in the wild. In addition to models trained in these datasets, fine-tuning and image restoration strategies are explored too. To do a more meaningful evaluation, all the models are compared in the testing set of real underwater images. We show that methods obtain impressive results, mainly when trained with our real dataset, comparing with manually segmented ground truth, even using a relatively small number of labeled underwater training images.

scholarly journals A novel deep learning-based 3D cell segmentation framework for future image-based disease detection

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Andong Wang ◽  
Qi Zhang ◽  
Yang Han ◽  
Sean Megason ◽  
Sahand Hormoz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clustering Algorithm ◽  
Fine Tuning ◽  
Cell Segmentation ◽  
Clinical Tool ◽  
Recent Success ◽  
Segmentation Methods ◽  
Cell Shapes ◽  
The Individual ◽  
Pathological Image

AbstractCell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell membrane images. Existing approaches also require fine-tuning multiple manually selected hyperparameters on the new datasets. We develop a deep learning-based 3D cell segmentation pipeline, 3DCellSeg, to address these challenges. Compared to the existing methods, our approach carries the following novelties: (1) a robust two-stage pipeline, requiring only one hyperparameter; (2) a light-weight deep convolutional neural network (3DCellSegNet) to efficiently output voxel-wise masks; (3) a custom loss function (3DCellSeg Loss) to tackle the clumped cell problem; and (4) an efficient touching area-based clustering algorithm (TASCAN) to separate 3D cells from the foreground masks. Cell segmentation experiments conducted on four different cell datasets show that 3DCellSeg outperforms the baseline models on the ATAS (plant), HMS (animal), and LRP (plant) datasets with an overall accuracy of 95.6%, 76.4%, and 74.7%, respectively, while achieving an accuracy comparable to the baselines on the Ovules (plant) dataset with an overall accuracy of 82.2%. Ablation studies show that the individual improvements in accuracy is attributable to 3DCellSegNet, 3DCellSeg Loss, and TASCAN, with the 3DCellSeg demonstrating robustness across different datasets and cell shapes. Our results suggest that 3DCellSeg can serve a powerful biomedical and clinical tool, such as histo-pathological image analysis, for cancer diagnosis and grading.

Unbiased estimation of particle number and sizes using the new stereological tools: the disector, the fractionator, the selector, and the nucleator — or just point-sampled intercepts

1988 ◽  
Vol 46 ◽  
pp. 428-429
Author(s):  
H.J.G. Gundersen
Keyword(s):  
Particle Number ◽  
Small Distance ◽  
Parallel Plane ◽  
Unbiased Estimation ◽  
Severe Problem ◽  
New Era ◽  
Size And Shape ◽  
Distinct Property ◽  
Sectioned Tissue

Previously, all stereological estimation of particle number and sizes were based on models and notoriously gave biased results, were very inefficient to use and difficult to justify. For all references to old methods and a direct comparison with unbiased methods see recent reviews.The publication in 1984 of the DISECTOR, the first unbiased stereological probe for sampling and counting 3—D objects irrespective of their size and shape, signalled the new era in stereology — and give rise to a number of remarkably simple and efficient techniques based on its distinct property: It is the only known way to obtain an unbiased sample of 3-D objects (cells, organelles, etc). The principle is simple: within a 2-D unbiased frame count or sample only cells which are not hit by a parallel plane at a known, small distance h.The area of the frame and h must be known, which might sometimes in itself be a problem, albeit usually a small one. A more severe problem may arise because these constants are known at the scale of the fixed, embedded and sectioned tissue which is often shrunken considerably.

Fluorescent potentiometric dyes for membrane-potential imaging

1994 ◽  
Vol 52 ◽  
pp. 166-167
Author(s):  
Leslie M. Loew
Keyword(s):  
Membrane Potential ◽  
Single Cells ◽  
Quantitative Imaging ◽  
Optical Recording ◽  
Biological Processes ◽  
Major Focus ◽  
The Past ◽  
Excitable Systems ◽  
Major Application ◽  
The Impact

A major application of potentiometric dyes has been the multisite optical recording of electrical activity in excitable systems. After being championed by L.B. Cohen and his colleagues for the past 20 years, the impact of this technology is rapidly being felt and is spreading to an increasing number of neuroscience laboratories. A second class of experiments involves using dyes to image membrane potential distributions in single cells by digital imaging microscopy - a major focus of this lab. These studies usually do not require the temporal resolution of multisite optical recording, being primarily focussed on slow cell biological processes, and therefore can achieve much higher spatial resolution. We have developed 2 methods for quantitative imaging of membrane potential. One method uses dual wavelength imaging of membrane-staining dyes and the other uses quantitative 3D imaging of a fluorescent lipophilic cation; the dyes used in each case were synthesized for this purpose in this laboratory.

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