Gray-Level Co-Occurrence Matrix Analysis of Granule Neurons of the Hippocampal Dentate Gyrus Following Cortical Injury

2020 ◽  
Vol 26 (1) ◽  
pp. 166-172
Author(s):  
Igor Pantic ◽  
Rada Jeremic ◽  
Sanja Dacic ◽  
Sanja Pekovic ◽  
Senka Pantic ◽  
...  

AbstractTraumatic brain injury (TBI) is a main cause of death and disabilities in young adults. Although learning and memory impairments are a major clinical manifestation of TBI, the consequences of TBI on the hippocampus are still not well understood. In particular, how lesions to the sensorimotor cortex damage the hippocampus, to which it is not directly connected, is still elusive. Here, we study the effects of sensorimotor cortex ablation (SCA) on the hippocampal dentate gyrus, by applying a highly sensitive gray-level co-occurrence matrix (GLCM) analysis. Using GLCM analysis of granule neurons, we discovered, in our TBI paradigm, subtle changes in granule cell (GC) morphology, including textual uniformity, contrast, and variance, which is not detected by conventional microscopy. We conclude that sensorimotor cortex trauma leads to specific changes in the hippocampus that advance our understanding of the cellular underpinnings of cognitive impairments in TBI. Moreover, we identified GLCM analysis as a highly sensitive method to detect subtle changes in the GC layers that is expected to significantly improve further studies investigating the impact of TBI on hippocampal neuropathology.

2021 ◽  
pp. 1-7
Author(s):  
Lazar M. Davidovic ◽  
Jelena Cumic ◽  
Stefan Dugalic ◽  
Sreten Vicentic ◽  
Zoran Sevarac ◽  
...  

Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.


2021 ◽  
pp. 1-8
Author(s):  
Jovana Paunovic ◽  
Danijela Vucevic ◽  
Tatjana Radosavljevic ◽  
Biserka Vukomanovic Djurdjevic ◽  
Sanja Stankovic ◽  
...  

Abstract


2005 ◽  
Vol 82 (5) ◽  
pp. 609-621 ◽  
Author(s):  
Kiyokazu Ogita ◽  
Norito Nishiyama ◽  
Chie Sugiyama ◽  
Kei Higuchi ◽  
Masanori Yoneyama ◽  
...  

2017 ◽  
Vol 11 ◽  
Author(s):  
Noemí Pallas-Bazarra ◽  
Asta Kastanauskaite ◽  
Jesús Avila ◽  
Javier DeFelipe ◽  
María Llorens-Martín

2019 ◽  
Vol 61 (4) ◽  
pp. 568-576
Author(s):  
Yaoyao He ◽  
Yi Rong ◽  
Hao Chen ◽  
Zhaoxi Zhang ◽  
Jianfeng Qiu ◽  
...  

Background The impact of variable b-value combinations on apparent diffusion coefficient (ADC)-based radiomics features has not been fully addressed in literature. Purpose To investigate the correlation between radiomics features extracted from ADC maps and various b-value combinations in cervical cancer. Material and Methods Diffusion-weighted images (b-values: 0, 600, 800, and 1000 s/mm2) of 20 patients with cervical cancer were included. Tumors were identified with the largest transversal cross-section and manually segmented by radiologist. For each b-value combination, 92 radiomics features were extracted and coefficient of variance (CV) was used to evaluate the robustness of radiomics features with different b-value combinations. Features with CV > 5% were normalized by the mean feature variation across the group. Results Out of a total of 92 radiomics features, 18 were classified as robust features with CV ≤5%. Among the rest (CV > 5%), 11, 23, and 40 features demonstrated 5%< CV ≤10%, 10%< CV ≤20%, and CV > 20%, respectively. A subset of features in each category (CV > 5%) showed strong correlation with the b-value combination variation, including 44% (7/16) features in gray level co-occurrence matrix, 62% (8/13) features in gray level dependence matrix, 64% (9/14) features in first order, 50% (8/16) features in gray level run length matrix, 57% (8/14) features in gray level size matrix, and 20% (1/5) features in neighborhood gray-tone difference matrix. Conclusions Variations in b-value combinations demonstrated impact on radiomics features extracted from ADC maps for cervical cancer. The radiomics features with CV <5% can be considered as robust features and are recommended to be used in multicenter radiomics studies.


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