scholarly journals Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Jun Cheng ◽  
Yuting Liu ◽  
Wei Huang ◽  
Wenhui Hong ◽  
Lingling Wang ◽  
...  
Keyword(s):  
Image Analysis ◽  
High Risk ◽  
Gastric Adenocarcinoma ◽  
Prognostic Model ◽  
Somatic Mutations ◽  
Patient Survival ◽  
Risk Index ◽  
Image Features ◽  
Automated Image Analysis ◽  
Histopathological Image

Computational analysis of histopathological images can identify sub-visual objective image features that may not be visually distinguishable by human eyes, and hence provides better modeling of disease phenotypes. This study aims to investigate whether specific image features are associated with somatic mutations and patient survival in gastric adenocarcinoma (sample size = 310). An automated image analysis pipeline was developed to extract quantitative morphological features from H&E stained whole-slide images. We found that four frequently somatically mutated genes (TP53, ARID1A, OBSCN, and PIK3CA) were significantly associated with tumor morphological changes. A prognostic model built on the image features significantly stratified patients into low-risk and high-risk groups (log-rank test p-value = 2.6e-4). Multivariable Cox regression showed the model predicted risk index was an additional prognostic factor besides tumor grade and stage. Gene ontology enrichment analysis showed that the genes whose expressions mostly correlated with the contributing features in the prognostic model were enriched on biological processes such as cell cycle and muscle contraction. These results demonstrate that histopathological image features can reflect underlying somatic mutations and identify high-risk patients that may benefit from more precise treatment regimens. Both the image features and pipeline are highly interpretable to enable translational applications.

scholarly journals Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Hui Li ◽  
Linyan Chen ◽  
Hao Zeng ◽  
Qimeng Liao ◽  
Jianrui Ji ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Model ◽  
Risk Group ◽  
Colon Adenocarcinoma ◽  
Image Features ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Module ◽  
Histopathological Image ◽  
Histopathological Images

BackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.MethodsWe downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).ResultsThere were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.ConclusionsThese results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.

Automated image analysis of nuclear atypia in high-power field histopathological image

2015 ◽  
Vol 258 (3) ◽  
pp. 233-240 ◽  
Author(s):  
CHENG LU ◽  
MENGYAO JI ◽  
ZHEN MA ◽  
MRINAL MANDAL
Keyword(s):  
Image Analysis ◽  
High Power ◽  
Automated Image Analysis ◽  
High Power Field ◽  
Nuclear Atypia ◽  
Histopathological Image

scholarly journals Automated Image Analysis for High-Content Screening and Analysis

2010 ◽  
Vol 15 (7) ◽  
pp. 726-734 ◽  
Author(s):  
Aabid Shariff ◽  
Joshua Kangas ◽  
Luis Pedro Coelho ◽  
Shannon Quinn ◽  
Robert F. Murphy
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Cell Biology ◽  
Image Data ◽  
Automated Analysis ◽  
Image Features ◽  
Automated Image Analysis ◽  
High Content Screening ◽  
Spatial Transformation

The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.

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.

Progress In The Practical Application Of Automated Image Analysis To Tem Negatives

1977 ◽  
Vol 35 ◽  
pp. 214-217
Author(s):  
F. A. Heckman ◽  
E. Redman ◽  
J.E. Connolly
Keyword(s):  
Image Analysis ◽  
Image Quality ◽  
Exposure Time ◽  
Image Contrast ◽  
Automated Image Analysis ◽  
Practical Application ◽  
Factors Affecting ◽  
High Image Quality ◽  
Qualitative Evidence ◽  
Comprehensive Study

In our initial publication on this subject1) we reported results demonstrating that contrast is the most important factor in producing the high image quality required for reliable image analysis. We also listed the factors which enhance contrast in order of the experimentally determined magnitude of their effect. The two most powerful factors affecting image contrast attainable with sheet film are beam intensity and KV. At that time we had only qualitative evidence for the ranking of enhancing factors. Later we carried out the densitometric measurements which led to the results outlined below.Meaningful evaluations of the cause-effect relationships among the considerable number of variables in preparing EM negatives depend on doing things in a systematic way, varying only one parameter at a time. Unless otherwise noted, we adhered to the following procedure evolved during our comprehensive study:Philips EM-300; 30μ objective aperature; magnification 7000- 12000X, exposure time 1 second, anti-contamination device operating.

Interfacing of a TEM/STEM System to a Computer

1981 ◽  
Vol 39 ◽  
pp. 250-251
Author(s):  
P. Hagemann
Keyword(s):  
Image Analysis ◽  
Transmission Rate ◽  
Analytical Electron Microscopy ◽  
Signal Beam ◽  
Automated Image Analysis ◽  
Beam Deflection ◽  
External Control ◽  
Computer Input ◽  
Instrument Control ◽  
Data Acquisition And Processing

The use of computers in the analytical electron microscopy today shows three different trends (1) automated image analysis with dedicated computer systems, (2) instrument control by microprocessors and (3) data acquisition and processing e.g. X-ray or EEL Spectroscopy.While image analysis in the T.E.M. usually needs a television chain to get a sequential transmission suitable as computer input, the STEM system already has this necessary facility. For the EM400T-STEM system therefore an interface was developed, that allows external control of the beam deflection in TEM as well as the control of the STEM probe and video signal/beam brightness on the STEM screen.The interface sends and receives analogue signals so that the transmission rate is determined by the convertors in the actual computer periphery.

scholarly journals Author Correction: Efficient microbial colony growth dynamics quantification with ColTapp, an automated image analysis application

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Julian Bär ◽  
Mathilde Boumasmoud ◽  
Roger D. Kouyos ◽  
Annelies S. Zinkernagel ◽  
Clément Vulin
Keyword(s):  
Image Analysis ◽  
Growth Dynamics ◽  
Colony Growth ◽  
Automated Image Analysis ◽  
Analysis Application

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

scholarly journals Scoring of radiation-induced microuclei in cytokinesis-blocked human lymphocytes by automated image analysis

Cytometry ◽  
1994 ◽  
Vol 17 (2) ◽  
pp. 119-127 ◽  
Author(s):  
F. Verhaegen ◽  
A. Vral ◽  
J. Seuntjens ◽  
N. W. Schipper ◽  
L. de Ridder ◽  
...  
Keyword(s):  
Image Analysis ◽  
Human Lymphocytes ◽  
Automated Image Analysis ◽  
Radiation Induced

An automated image analysis platform for the study of weakly -adhered cells

Biofouling ◽  
2021 ◽  
pp. 1-10
Author(s):  
Zhijing Wan ◽  
Ben T. MacVicar ◽  
Shea Wyatt ◽  
Diana E. Varela ◽  
Rajkumar Padmawar ◽  
...  
Keyword(s):  
Image Analysis ◽  
Automated Image Analysis ◽  
Analysis Platform

scholarly journals Yeast Morphology Assessment through Automated Image Analysis during Fermentation

Fermentation ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 44
Author(s):  
Mario Guadalupe-Daqui ◽  
Mandi Chen ◽  
Katherine A. Thompson-Witrick ◽  
Andrew J. MacIntosh
Keyword(s):  
Image Analysis ◽  
High Stress ◽  
Cross Sectional Area ◽  
Automated Image Analysis ◽  
Yeast Cells ◽  
Morphological Properties ◽  
Cell Area ◽  
Sectional Area ◽  
Cross Sectional ◽  
Industrial Fermentation

The kinetics and success of an industrial fermentation are dependent upon the health of the microorganism(s) responsible. Saccharomyces sp. are the most commonly used organisms in food and beverage production; consequently, many metrics of yeast health and stress have been previously correlated with morphological changes to fermentations kinetics. Many researchers and industries use machine vision to count yeast and assess health through dyes and image analysis. This study assessed known physical differences through automated image analysis taken throughout ongoing high stress fermentations at various temperatures (30 °C and 35 °C). Measured parameters included sugar consumption rate, number of yeast cells in suspension, yeast cross-sectional area, and vacuole cross-sectional area. The cell morphological properties were analyzed automatically using ImageJ software and validated using manual assessment. It was found that there were significant changes in cell area and ratio of vacuole to cell area over the fermentation. These changes were temperature dependent. The changes in morphology have implications for rates of cellular reactions and efficiency within industrial fermentation processes. The use of automated image analysis to quantify these parameters is possible using currently available systems and will provide additional tools to enhance our understanding of the fermentation process.

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