Segmentation Of Microscopy Images Research Articles

SARS-CoV-2: theoretical analysis of the proposed algorithms to the enhancement and segmentation of high-resolution microscopy images-Part II.

Today is a reality that the novel coronavirus SARS-Cov-2 has become a global pandemic. For this reason, the study of real microscopic images of this coronavirus is of great importance, as it allows us to carry out a more precise research on it. However, as we pointed out in a former paper as reported by Roberto Rodríguez (SARS-CoV-2: Enhancement and Segmentation of High-Resolution Microscopy Images. Part I”, Sent to Signal, Image and Video Processing Video Processing, Springer, New York, 2020), many times these microscopic images present some blurring problems, which are always susceptible to be improved. The aim of this work is to carry out a theoretical analysis of the proposed algorithms to enhancement and segmentation of these microscopic images, which is important for the design and development of future algorithms before new epidemics.

An Improved SLIC Algorithm for Segmentation of Microscopic Cell Images

Accurate nuclear and cell segmentations plays an important role in improving the accuracy of target recognition in microscopic cell images. As the traditional SLIC (Simple Linear Iterative Clustering) algorithm cannot segment microscopic cell images well, an improved SLIC superpixel segmentation algorithm based on gray scale enhancement and regional equalization is proposed. According to the characteristics of microscopic cell images, selecting different transformation parameters with the conditional iterative algorithm, the best classification multi-threshold method based on maximum entropy criterion is used to nonlinearly enhance the gray scale of the original images, while enhancing the contrast of the image, it also greatly improves the balance of each classification region. Then the gray distance and spatial distance are calculated respectively in the circle neighborhood of the cluster center to realize the superpixel segmentation of the image. Finally, the improved SLIC algorithm and the comparison algorithm are tested and evaluated. The experimental results show that our improved SLIC algorithm model has higher segmentation accuracy and is more suitable for cell segmentation in microscopic cell images than original SLIC algorithm.

The Active Segmentation Platform for Microscopic Image Classification and Segmentation.

Image segmentation still represents an active area of research since no universal solution can be identified. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. The article presents an automated image segmentation and classification platform, called Active Segmentation, which is based on ImageJ. The platform integrates expert domain knowledge, providing partial ground truth, with geometrical feature extraction based on multi-scale signal processing combined with machine learning. The approach in image segmentation is exemplified on the ISBI 2012 image segmentation challenge data set. As a second application we demonstrate whole image classification functionality based on the same principles. The approach is exemplified using the HeLa and HEp-2 data sets. Obtained results indicate that feature space enrichment properly balanced with feature selection functionality can achieve performance comparable to deep learning approaches. In summary, differential geometry can substantially improve the outcome of machine learning since it can enrich the underlying feature space with new geometrical invariant objects.

Automated Image Segmentation of the Corneal Endothelium in Patients With Fuchs Dystrophy.

PurposeTo perform segmentation of specular microscopy (SM) images of the corneal endothelium for comparing average perimeter length (APL) between Fuchs endothelial corneal dystrophy (FECD) patients and healthy subjects.MethodsA retrospective review of clinical records of FECD patients and those with healthy endothelium was carried out to collect images of the endothelium. The images were segmented by modified U-Net, a deep learning architecture, followed by the Watershed algorithm to resolve merged cell borders (<5%). The segmented images were analyzed for endothelial cell density (ECDUW) and APL.ResultsThe combination of the U-Net and Watershed algorithm, referred to as the UW approach, enabled a complete segmentation of the endothelium. In healthy, ECDUW was close to estimates by SM and manual segmentation (31 subjects; P > 0.1). However, in FECD, ECDUW was closer to estimates by manual segmentation but not by SM (27 patients; P < 0.001). ECDUW in FECD (2547 ± 499 cells/mm2; 60 patients) was smaller compared to that in the healthy (2713 ± 401 cells/mm2; 70 subjects) (P < 0.001). APL in the healthy was 66.87 ± 7.68 µm/cell (70 subjects), but it increased with %Guttae in FECD (56.60–195.30 µm/cell; 60 patients) (P < 0.0001).ConclusionsThe UW approach is precise for the segmentation of SM images from the healthy and FECD. Our analysis has revealed that APL increases with %Guttae.Translational RelevanceThe average perimeter length of the corneal endothelium, which represents the length of the paracellular pathway for fluid flux into the stroma, is increased in Fuchs dystrophy.

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

Automatic segmentation of key microstructural features in atomic-scale electron microscope images is critical to improved understanding of structure–property relationships in many important materials and chemical systems. However, the present paradigm involves time-intensive manual analysis that is inherently biased, error-prone, and unable to accommodate the large volumes of data produced by modern instrumentation. While more automated approaches have been proposed, many are not robust to a high variety of data, and do not generalize well to diverse microstructural features and material systems. Here, we present a flexible, semi-supervised few-shot machine learning approach for segmentation of scanning transmission electron microscopy images of three oxide material systems: (1) epitaxial heterostructures of SrTiO3/Ge, (2) La0.8Sr0.2FeO3 thin films, and (3) MoO3 nanoparticles. We demonstrate that the few-shot learning method is more robust against noise, more reconfigurable, and requires less data than conventional image analysis methods. This approach can enable rapid image classification and microstructural feature mapping needed for emerging high-throughput characterization and autonomous microscope platforms.

Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy

Multi-threshold image segmentation (MIS) is now a well known image segmentation technique, and many researchers have applied intelligent algorithms to it, but these methods suffer from local optimal drawbacks. This paper presented a novel approach to improve the Salp Swarm Algorithm (SSA), namely EHSSA, and applied it to MIS. Knowing the inaccuracies and discussions on implementation of this method, a new efficient mechanism is proposed to improve global search capability of the algorithm and avoid falling into a local optimum. Moreover, the excellence of the proposed algorithm was proved by comparative experiments at IEEE CEC2014. Afterward, the performance of EHSSA was demonstrated by testing a set of images selected from the Berkeley segmentation data set 500 (BSDS500), and the experimental results were analyzed by evaluating the parameters, which proved the efficiency of the proposed algorithm in MIS. Furthermore, EHSSA was applied to the microscopic image segmentation of breast cancer. Medical image segmentation is the study of how to quickly extract objects of interest (human organs) from various images to perform qualitative and quantitative analysis of diseased tissues and improve the accuracy of their diagnosis, which assists the physician in making more informed decisions and patient rehabilitation. The results of this set of experiments also proved its superior performance. For any info about this paper, readers can refer to https://aliasgharheidari.com.

Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images.

Detection, segmentation, and quantification of microvascular structures are the main steps towards studying microvascular remodeling. Combined with appropriate staining, confocal microscopy imaging enables exploration of the full 3D anatomical characteristics of microvascular systems. Segmentation of confocal microscopy images is a challenging task due to complexity of anatomical structures, staining and imaging issues, and lack of annotated training data. In this paper, we propose a deep learning system for robust segmentation of cranial vasculature of mice in confocal microscopy images. The proposed system is an ensemble of two deep-learning cascades consisting of two coarse-to-fine subnetworks with skip connections in between. One cascade aims to improve sensitivity, while the other aims to improve precision of the segmentation results. Our experiments on mice cranial vasculature showed promising results achieving segmentation accuracy of 92.02% and dice score of 81.45% despite being trained on very limited confocal microscopy data.

Automated Microscopy Image Segmentation and Analysis with Machine Learning.

The development of automated quantitative image analysis pipelines requires thoughtful considerations to extract meaningful information. Commonly, extraction rules for quantitative parameters are defined and agreed beforehand to ensure repeatability between annotators. Machine/Deep Learning (ML/DL) now provides tools to automatically extract the set of rules to obtain quantitative information from the images (e.g. segmentation, enumeration, classification, etc.). Many parameters must be considered in the development of proper ML/DL pipelines. We herein present the important vocabulary, the necessary steps to create a thorough image segmentation pipeline, and also discuss technical aspects that should be considered in the development of automated image analysis pipelines through ML/DL.

CS-Net: Instance-aware cellular segmentation with hierarchical dimension-decomposed convolutions and slice-attentive learning

Cellular segmentation in kinds of microscopy images is a fundamental prerequisite in the workflow of many biomedical applications. However, accurate segmentation of plenty of cellular instances faces several challenges, including instance-size imbalance, clustered cellular instances, fuzzy boundaries, and cell types’ heterogeneity. Moreover, both 2D and 3D data should be processed with scalability. To address these challenges, we present a lightweight slice-wise CS-Net building on novel hierarchical dimension-decomposed (HDD) convolutions and a novel instance-aware loss for both 2D and 3D microscopy image segmentation. To capture inter-slice contexts in 3D data, we exploit both 2.5D input and 2.5D supervision, and introduce CS-Net (2.5D++) for 3D segmentation tasks. Specifically, we devise a slice-aware encoder to extract diverse and multiscale contexts from multi-slice input and a slice-attentive decoder to take advantage of slice-wise 2.5D supervision. To separate clustered instances, we augment the segmentation task with an edge prediction task, the output of which is used to separate touching instances. Extensive experiments on 3D multi-tissue electron microscopy (EM) data and 2D histology images demonstrate that our 2D and 2.5D methods achieve state-of-the-art performance with scalability to diverse data. The comparative results with top-performing lightweight models also indicate that the proposed model shows a better balance of segmentation performance and computational complexity.

PyJAMAS: open-source, multimodal segmentation and analysis of microscopy images.

Our increasing ability to resolve fine details using light microscopy is matched by an increasing need to quantify images in order to detect and measure phenotypes. Despite their central role in cell biology, many image analysis tools require a financial investment, are released as proprietary software, or are implemented in languages not friendly for beginners, and thus are used as black boxes. To overcome these limitations, we have developed PyJAMAS, an open-source tool for image processing and analysis written in Python. PyJAMAS provides a variety of segmentation tools, including watershed and machine learning-based methods; takes advantage of Jupyter notebooks for the display and reproducibility of data analyses; and can be used through a cross-platform graphical user interface or as part of Python scripts via a comprehensive application programming interface. PyJAMAS is open-source and available at https://bitbucket.org/rfg_lab/pyjamas. Supplementary data are available at Bioinformatics online.

EFFICACY OF CIELAB AND CMYK COLOR SPACES IN LEUKEMIA IMAGE ANALYSIS: A COMPARISON BY STATISTICAL TECHNIQUES

There are several color models available and which color model to be used is a question to many researchers in the field of Medical Image Processing and Analysis. In this paper, the suitability of using the CIELAB and CMYK color spaces in processing and analyzing blood smear images of leukemia is assessed for comparison. Leukemia is commonly known as blood cancer and it usually leads to an abnormal proliferation of leukocytes (White Blood Cells) in the bone marrow and blood. The diagnosis of leukemia is primarily done by Pathologists by microscopically examining the blood and bone marrow smears of the suspected patient. Image processing-based methods can be used for automated detection and classification of leukemia to assist the Pathologists for a speedy diagnosis. The proposed study used [Formula: see text]-means clustering to segment the leukocytes and leukemic blast cells. The microscopic smear images in RGB color format were transformed and processed in CIELAB as well as CMYK color spaces for comparison. Statistical analysis was performed using Student’s [Formula: see text] test. The visual observation of the segmented images revealed that, processing the images in the CIELAB color space is comparatively better than the CMYK color space and it is reflected in the results of statistical comparison by Student’s [Formula: see text] test. The study assessed the suitability of using the CIELAB and CMYK color spaces in segmentation and analysis of microscopic smear images of Leukemic and Normal cases and found that processing the images in CIELAB color space is comparatively better than CMYK and such comparative analysis is not available in the literature.

Prognostication of Acute Lymphocytic Leukemia (ALL) using Capsule Network Algorithm

A type of cancer that affects the blood-forming tissues in the body including lymphatic system and bone marrow is Leukemia. The second most commonly occurring acute leukemia is the acute lymphoblastic leukemia or acute lymphocytic leukemia (ALL). Around 25% of the cases are observed to be due to malignant T-cell precursors while the remaining 75% of cases is due to precursors of B-cell lineage. In general, response to chemotherapy, white blood cell count and age are the clinical factors that contribute towards risk stratification. However, in recent years it has been identified that genetic alterations have enabled between individual prognosis and recovery. Despite advancement in technology, chemotherapy using anthracycline, corticosteroids and vincristine serves to be the backbone therapy to treat this disease. In this proposed work, we have used a deep convolutional neural network to detect the presence of ALL accurately and based on the image screened, it is further categorized into one of the 4 subclasses. Using Capsule network algorithm (CapsNet), we have established 100% average sensitivity for ALL detection with a highest specificity of 99.56%, precision of 99.82% and accuracy of 99.36%. When compared with other similar methodologies, we have been able to accomplish higher accuracy without microscopic image segmentation using capsule network algorithm.

Swarm Intelligence-Based Methodology for Scanning Electron Microscope Image Segmentation of Solid Oxide Fuel Cell Anode

Segmentation of images from scanning electron microscope, especially multiphase, poses a drawback in their microstructure quantification process. The labeling process must be automatized due to the time consumption and irreproducibility of the manual labeling procedure. Here we show a swarm intelligence-driven filtration methodology performed on raw solid oxide fuel cell anode’s material images to improve the segmentation methods’ performance. The methodology focused on two significant parts of the segmentation process, which are filtering and labeling. During the first one, the images underwent filtering by applying a series of filters, whose operation parameters were determined using Particle Swarm Optimization upon a dedicated cost function. Next, Seeded Region Growing, k-Means Clustering, Multithresholding, and Simple Linear Iterative Clustering Superpixel algorithms were utilized to label the filtered images’ regions into consecutive phases in the microstructure. The improvement was presented for three different metrics: the Misclassification Ratio, Structural Similarity Index Measure, and Mean Squared Error. The obtained distribution of metrics’ performances was based on 200 images, with and without filtering. Results indicate an improvement up to 29%, depending on the metric and method used. The presented work contributes to the ongoing efforts to automatize segmentation processes fully for an increasing number of tomographic measurements, particularly in solid oxide fuel cell research.

Optimized Segmentation of Microscopic Images by Otsu’ Method to Find White Blood Cells

In the medical field, microscopic image-based investigations are broadly used to examine cell morphology and for disease diagnosis. In this article white blood cells (WBC) are identified from microscopic images by image segmentation. One of the important segmentation methods is multilevel thresholding of an image, in this process pixels are grouped into different classes depends on thresholding levels. The selection of threshold levels affects the efficiency of segmentation. Otsu’s method is a significant and important segmentation technique based on the multi-threshold of the histogram. Optimization techniques can be used to compute optimized threshold levels, Harmony Search Algorithm (HSA) is used for this purpose. From the color, microscopic image red, green, and blue components are extracted and segmentation to find white blood cells, and from the same images gray, hue, saturation, and intensity components also extracted pathological features. After segmentation Morphological opening and closing are applied for an efficient finding of white cells, from results the green component of microscopic images is giving efficient results. Results are described by using standard deviation (STDR), mean square error (MSE), Mean of fitness of algorithm (MOFIT), time for execution, and FITNESS function.

SARS-CoV-2: enhancement and segmentation of high-resolution microscopy images-Part I.

Possibly, and due to poor eating habits and unhealthy lifestyle, many viruses are transmitted to human people. Such is the case, of the novel coronavirus SARS-Cov-2, which has expanded of exponential way, practically, to whole world population. For this reason, the enhancement of real microscopic images of this coronavirus is of great importance. Of this way, one can highlight the S-spikes and visualizing those areas that show a high density, which are related to active zones of viral germination and major spread of the virus. The SARS-Cov-2 images were captured from nasopharyngeal samples of Cuban symptomatic individuals (RT-PCR positives for SARS-CoV-2) and processed via scanning electron microscopy. However, many times these microscopic images present some blurring problems, and the S-spikes do not look well defined. Therefore, the aim of this work is to propose new computational methods to carry out enhancement and segmentation of SARS-Cov-2 high-resolution microscopic images. The proposed strategy obtained very satisfactory results, and we validated its performance, together with specialist physicians, on a set of 1005 images. Due to the importance of the obtained results, this first work will be addressed to the application of the proposed algorithm. A second paper will deeply analyze the theory related to these algorithms.

Microscopic Image Segmentation and Morphological Characterization of Novel Chitosan/Silica Nanoparticle/Nisin Films Using Antimicrobial Technique for Blueberry Preservation.

In the current work, the characterization of novel chitosan/silica nanoparticle/nisin films with the addition of nisin as an antimicrobial technique for blueberry preservation during storage is investigated. Chitosan/Silica Nanoparticle/N (CH-SN-N) films presented a stable suspension as the surface loads (45.9 mV) and the distribution was considered broad (0.62). The result shows that the pH value was increased gradually with the addition of nisin to 4.12, while the turbidity was the highest at 0.39. The content of the insoluble matter and contact angle were the highest for the Chitosan/Silica Nanoparticle (CH-SN) film at 5.68%. The use of nano-materials in chitosan films decreased the material ductility, reduced the tensile strength and elongation-at-break of the membrane. The coated blueberries with Chitosan/Silica Nanoparticle/N films reported the lowest microbial contamination counts at 2.82 log CFU/g followed by Chitosan/Silica Nanoparticle at 3.73 and 3.58 log CFU/g for the aerobic bacteria, molds, and yeasts population, respectively. It was observed that (CH) film extracted 94 regions with an average size of 449.10, at the same time (CH-SN) film extracted 169 regions with an average size of 130.53. The (CH-SN-N) film presented the best result at 5.19%. It could be observed that the size of the total region of the fruit for the (CH) case was the smallest (1663 pixels), which implied that the fruit lost moisture content. As a conclusion, (CH-SN-N) film is recommended for blueberry preservation to prolong the shelf-life during storage.

White Blood Cells Segmentation and Classification Using Swarm Optimization Algorithms and Multilayer Perceptron

This study proposes a segmentation and classification system for early detection of blood disease; the proposed system consists of three phases. The first phase is segmenting white blood cells using multi-level thresholding optimized by the butterfly optimization algorithm to select the optimal threshold value to increase the accuracy. The second phase is extracting geometric and shape features of the segmented cells. The third phase is using the gray wolf optimizer to adopt the weights and biases of the multilayer perceptron to enhance the accuracy of classification between normal and leukemia cells, classify the normal cells to their five categories, and classify the leukemia to their four categories. The proposed system applies to different data sets (ALL-IDB2, LISC, and ASH-Image bank) and overcomes the segmentation and classification problems of microscopic images and shows an outstanding segmentation result, 98.02%; and the average classification accuracy between normal and leukemia cells is 98.58%, between white blood cell categories is 98.9%, and between leukemia types is 98.93%.

Microscopy images segmentation algorithm based on shearlet neural network

Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.

Abstract S11-04: Using CXR-Net to detect COVID-19 and non-COVID-19 patients

Abstract A critical step in the fight against COVID-19 pandemic is the screening and rapid recognition of those affected by Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2). The main screening method used for detecting COVID-19 infection is the reverse transcriptase-polymerase chain reaction (RT-PCR). Furthermore, the main screening methods for SARS-CoV-2 are chest X-ray (CXR) and computed tomography (CT). While CTs provide greater diagnostic accuracy, CXRs are more readily available and enable rapid triaging of patients in the most affected areas. Since CXR imaging is typically carried out as part of a standard procedure for patients with a respiratory complaint, it represents an ideal complement to RT-PCR testing. For this reason, much effort is ongoing into the development of Artificial Intelligence diagnostic systems based on Neural Networks (NN) that can aid radiologists in accurately interpreting CXRs in SARS-CoV-2 cases. Here we present CXR-Net, a two-module pipeline for SARS-CoV-2 detection. Module I is based on Res-CR-Net, a type of NN originally developed for the semantic segmentation of microscopy images (doi: 10.1088/2632-2153/aba8e8), which can process images of any size, retaining the original resolution of the input images in the feature maps of all layers and in the final output. Module I was trained on a dataset of 6395 Antero/Posterior CXRs with radiologist annotated lung contours to generate accurate masks of the lungs that overlap the heart and large vasa, and are minimally influenced by regions of consolidation or other texture alterations due to underlying pathologies. Module II is a hybrid convnet in which the first convolutional layer with learned coefficients is replaced by a layer with fixed coefficients provided by the Wavelet Scattering Transform (WST). A particular advantage of this hybrid net is the removal of deep network instabilities associated with adversarial images (noise or small deformations in the input images that are visually insignificant, but that the network does not reduce correctly, leading to incorrect classification). This net converges more rapidly than an end-to-end learned architecture, does not suffer from vanishing or exploding gradients, and prevents overfitting, leading to better generalization. Module II takes as inputs the patients’ CXRs and corresponding lung masks calculated by Module I, and produces as outputs a class assignment (Covid or non-Covid) and high resolution heat maps that identify the SARS associated lung regions. In preliminary work, CXR-Net was tested on a small dataset of CXRs from non-Covid and RT-PCR confirmed Covid patients acquired at HFHS Hospital in Detroit. Module II was trained against 405 CXRs, and validated against 84 CXRs (validation statistics: accuracy = 0.810; precision = 0.833; recall = 0.932; F1 score = 0.88, ROC_auc = 0.942). Citation Format: Benjamen Huber, Abdulah Haikal, Domenico Gatti, Hamid Soltanian-Zadeh. Using CXR-Net to detect COVID-19 and non-COVID-19 patients [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2021 Feb 3-5. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(6_Suppl):Abstract nr S11-04.

PartSeg: a tool for quantitative feature extraction from 3D microscopy images for dummies

BackgroundBioimaging techniques offer a robust tool for studying molecular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high-resolution 3D microscopy provides access to an ever-increasing amount of high-quality images, there arises a need for their analysis in an automated, unbiased, and simple way. Segmentation of structures within the cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap. At the same time, the available segmentation tools provide a steep learning curve for new users with a limited technical background. This is especially apparent in the bulk processing of image sets, which requires the use of some form of programming notation.ResultsIn this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of the cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images. The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer an ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results. Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins. For biologists, the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures.ConclusionsIn this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multi-scale segmentation algorithms best-suited for this task. PartSeg can also be used for the bulk processing of multiple images and its components can be reused in other systems or computational experiments.