scholarly journals MIA: An Open Source Standalone Deep Learning Application for Microscopic Image Analysis

2022 ◽  
Author(s):  
Nils Koerber
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Open Source ◽  
Imaging Techniques ◽  
Learning Algorithms ◽  
Automated Image Analysis ◽  
Image Analyzer ◽  
Microscopic Image ◽  
Biomedical Sciences ◽  
Model Training

In recent years the amount of data generated by imaging techniques has grown rapidly along with increasing computational power and the development of deep learning algorithms. To address the need for powerful automated image analysis tools for a broad range of applications in the biomedical sciences, we present the Microscopic Image Analyzer (MIA). MIA combines a graphical user interface that obviates the need for programming skills with state-of-the-art deep learning algorithms for segmentation, object detection, and classification. It runs as a standalone, platform-independent application and is compatible with commonly used open source software packages. The software provides a unified interface for easy image labeling, model training and inference. Furthermore the software was evaluated in a public competition and performed among the top three for all tested data sets. The source code is available on https://github.com/MIAnalyzer/MIA.

Automated Image Analysis Of Nuclear And Cytoplasmic Changes In Exfoliated Cells From Tracheas Treated With Carcinogen

1977 ◽  
Vol 35 ◽  
pp. 220-221
Author(s):  
S.F. Stinson ◽  
J.C. Lilga ◽  
M.B. Sporn
Keyword(s):  
Image Analysis ◽  
Pattern Analysis ◽  
Relative Proportion ◽  
Automated Image Analysis ◽  
Image Analyzer ◽  
Cross Sectional ◽  
Exfoliated Cells ◽  
Neoplastic Lesions ◽  
Analysis System ◽  
Morphologic Criterion

Increased nuclear size, resulting in an increase in the relative proportion of nuclear to cytoplasmic sizes, is an important morphologic criterion for the evaluation of neoplastic and pre-neoplastic cells. This paper describes investigations into the suitability of automated image analysis for quantitating changes in nuclear and cytoplasmic cross-sectional areas in exfoliated cells from tracheas treated with carcinogen.Neoplastic and pre-neoplastic lesions were induced in the tracheas of Syrian hamsters with the carcinogen N-methyl-N-nitrosourea. Cytology samples were collected intra-tracheally with a specially designed catheter (1) and stained by a modified Papanicolaou technique. Three cytology specimens were selected from animals with normal tracheas, 3 from animals with dysplastic changes, and 3 from animals with epidermoid carcinoma. One hundred randomly selected cells on each slide were analyzed with a Bausch and Lomb Pattern Analysis System automated image analyzer.

scholarly journals The Development of a Skin Image Analysis Tool by Using Machine Learning Algorithms

Cosmetics ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 67
Author(s):  
Perry Xiao ◽  
Xu Zhang ◽  
Wei Pan ◽  
Xiang Ou ◽  
Christos Bontozoglou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Imaging Techniques ◽  
Learning Algorithms ◽  
Research Work ◽  
Software Tool ◽  
Machine Learning Algorithms ◽  
Analysis Tool ◽  
Skin Imaging ◽  
User Friendly

We present our latest research work on the development of a skin image analysis tool by using machine-learning algorithms. Skin imaging is very import in skin research. Over the years, we have used and developed different types of skin imaging techniques. As the number of skin images and the type of skin images increase, there is a need of a dedicated skin image analysis tool. In this paper, we report the development of such software tool by using the latest MATLAB App Designer. It is simple, user friendly and yet powerful. We intend to make it available on GitHub, so that others can benefit from the software. This is an ongoing project; we are reporting here what we have achieved so far, and more functions will be added to the software in the future.

Image analysis and SEM characterization of SiC whiskers and ceramic composites

1988 ◽  
Vol 46 ◽  
pp. 746-747
Author(s):  
R. T. Chen ◽  
M. G. Jamieson
Keyword(s):  
Image Analysis ◽  
Ceramic Composites ◽  
Optical Microscope ◽  
Automated Image Analysis ◽  
Critical Parameters ◽  
Image Analyzer ◽  
Aspect Ratios ◽  
Structural Applications ◽  
Sic Whiskers ◽  
Dispersion Technique

Recently, attention has been focused on whisker reinforced ceramic composites Ipecause of their potential in high temperature structural applications. Aspect ratios of whiskers and particle size distributions of the matrix ceramic powders are critical parameters which can effect processing and microstructure/properties of the final composite materials. In this paper, quantification of these parameters using SEM and automated image analysis, as well as their significance in the development of whisker reinforced ceramic composites will be discussed.The present study involved the evaluation of microstructures of SiC reinforced alumina ceramic composites. In the evaluation of SiC whiskers, good whisker dispersal is a prerequisite for automated image analysis. A dispersion technique was used to break up the agglomerated SiC whiskers. Whiskers were dispersed on glass slides and aspect ratios were measured using a Quantimet-970 image analyzer interfaced to an optical microscope. Because of the submicron nature of the starting alumina powders, SEM was needed to provide images. For grain size evaluation, both fracture surfaces and polished/etched surfaces were examined in a JEOL JXA-840 SEM.

Fine-Aggregate Angularity: Automated Image Analysis Approach

2000 ◽  
Vol 1721 (1) ◽  
pp. 66-72 ◽  
Author(s):  
Eyad Masad ◽  
Joe W. Button ◽  
Tom Papagiannakis
Keyword(s):  
Image Analysis ◽  
Visual Analysis ◽  
Imaging Techniques ◽  
Permanent Deformation ◽  
Asphalt Mixtures ◽  
Automated Image Analysis ◽  
Fine Aggregate ◽  
Smoothing Effect ◽  
Aggregate Properties ◽  
Wide Range

Angularity is one of the important aggregate properties contributing to the permanent deformation resistance of asphalt mixtures. Therefore, methods that are able to rapidly and accurately describe aggregate angularity are valuable in the design process of asphalt mixtures. Two computer-automated procedures, which make use of the advances in digital-image processing, to quantify fine aggregate angularity, are presented. The first method relies on the concepts of the erosion-dilation techniques. This consists of subjecting the aggregate surface to a smoothing effect that causes the angularity elements to disappear from the image. Then, the area lost as a result of the smoothing effect is calculated and used to quantify angularity. The second method is based on the fractal approach. Image-analysis techniques are used to measure the fractal length of aggregate boundary. The fractal length increases with aggregate angularity. The proposed imaging techniques are used to capture the aggregate angularity of 23 sand samples that represent a wide range of materials. The results are compared with visual analysis and indirect methods of measuring fine-aggregate angularity, such as the uncompacted air voids, and the angle of internal friction of aggregate mass. In general, the results indicate much promise for measuring aggregate properties using automated imaging technologies.

scholarly journals Pathology Image Analysis Using Segmentation Deep Learning Algorithms

2019 ◽  
Vol 189 (9) ◽  
pp. 1686-1698 ◽  
Author(s):  
Shidan Wang ◽  
Donghan M. Yang ◽  
Ruichen Rong ◽  
Xiaowei Zhan ◽  
Guanghua Xiao
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Pathology Image

scholarly journals ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

2021 ◽  
Vol 65 (2) ◽  
Author(s):  
Oleksandr Dudin ◽  
◽  
Ozar Mintser ◽  
Oksana Sulaieva ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Personalized Medicine ◽  
Digital Pathology ◽  
Automated Image Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Wide Range

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

Demystification of Deep Learning-Driven Medical Image Processing and Its Impact on Future Biomedical Applications

2020 ◽  
pp. 155-171
Author(s):  
R. Udendhran ◽  
Balamurugan M.
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Biomedical Applications ◽  
Medical Image Analysis ◽  
Learning Algorithms ◽  
New Era ◽  
Fast Development ◽  
Computational Technology

The recent growth of big data has ushered in a new era of deep learning algorithms in every sphere of technological advance, including medicine, as well as in medical imaging, particularly radiology. However, the recent achievements of deep learning, in particular biomedical applications, have, to some extent, masked decades-long developments in computational technology for medical image analysis. The methods of multi-modality medical imaging have been implemented in clinical as well as research studies. Due to the reason that multi-modal image analysis and deep learning algorithms have seen fast development and provide certain benefits to biomedical applications, this chapter presents the importance of deep learning-driven medical imaging applications, future advancements, and techniques to enhance biomedical applications by employing deep learning.

scholarly journals The radiological diagnosis of bronchiectasis: what's in a name?

2020 ◽  
Vol 29 (156) ◽  
pp. 190120
Author(s):  
Harm A.W.M. Tiddens ◽  
Jennifer J. Meerburg ◽  
Menno M. van der Eerden ◽  
Pierluigi Ciet
Keyword(s):  
Image Analysis ◽  
Lung Volume ◽  
State Of The Art ◽  
Imaging Techniques ◽  
Automated Image Analysis ◽  
Radiological Diagnosis ◽  
Specific Reference ◽  
Airway Wall ◽  
Gold Standard Method ◽  
Airway Dimensions

Diagnosis of bronchiectasis is usually made using chest computed tomography (CT) scan, the current gold standard method. A bronchiectatic airway can show abnormal widening and thickening of its airway wall. In addition, it can show an irregular wall and lack of tapering, and/or can be visible in the periphery of the lung. Its diagnosis is still largely expert based. More recently, it has become clear that airway dimensions on CT and therefore the diagnosis of bronchiectasis are highly dependent on lung volume. Hence, control of lung volume is required during CT acquisition to standardise the evaluation of airways. Automated image analysis systems are in development for the objective analysis of airway dimensions and for the diagnosis of bronchiectasis. To use these systems, clear and objective definitions for the diagnosis of bronchiectasis are needed. Furthermore, the use of these systems requires standardisation of CT protocols and of lung volume during chest CT acquisition. In addition, sex- and age-specific reference values are needed for image analysis outcome parameters. This review focusses on today's issues relating to the radiological diagnosis of bronchiectasis using state-of-the-art CT imaging techniques.

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