Automatic detection of hypoxia in renal tissue stained with HIF-1alpha

Buzin, Aline Rodrigues; Macedo, Nayana Damiani; Araujo, Isabela Bastos Binotti Abreu de; Nogueira, Breno Valentim; Andrade, Tadeu Uggere de; Endringer, Denise Coutinho; Lenz, Dominik

doi:10.1016/j.jim.2017.02.005

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…The present study demonstrated that automatic image analysis can be used to identify and quantify VEGF in tissue. Other studies identified HIF1a-positive cells [31] and TUNEL-positive cells [32] in renal tissue sections. Diem et al [4] used automatic image analysis to count CD4 + and CD8 + T cells in human tissue and stated that even for images with a high cell density the automated counting was approximately 10 minutes faster than manual counting.…”

Section: Discussionmentioning

confidence: 99%

Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections

Macedo

Buzin

Araujo

et al. 2019

Journal of Immunology Research

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Background. Manual analysis of tissue sections, such as for pathological diagnosis, requires an analyst with substantial knowledge and experience. Reproducible image analysis of biological samples is steadily gaining scientific importance. The aim of the present study was to employ image analysis followed by machine learning to identify vascular endothelial growth factor (VEGF) in kidney tissue that had been subjected to hypoxia. Methods. Light microscopy images of renal tissue sections stained for VEGF were analyzed. Subsequently, machine learning classified the cells as VEGF+ and VEGF- cells. Results. VEGF was detected and cells were counted with high sensitivity and specificity. Conclusion. With great clinical, diagnostic, and research potential, automatic image analysis offers a new quantitative capability, thereby adding numerical information to a mostly qualitative diagnostic approach.

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Section: Discussionmentioning

confidence: 99%

Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections

Macedo

Buzin

Araujo

et al. 2019

Journal of Immunology Research

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“…With the explosive development of deep learning (DL) [1], algorithms based on convolutional neural networks (CNNs) have achieved great success in target detection [2][3][4][5][6]. This type of algorithms has been effectively applied to solve problems in various industries [7][8][9][10]. Compared with traditional target detection algorithms, CNN-based algorithms support datadriven feature extraction with the aid of artificial neural networks (ANNs), acquiring deep abstract features of a specific dataset after learning numerous samples.…”

Section: Introductionmentioning

confidence: 99%

Detection of Dense Small Rigid Targets Based on Convolutional Neural Network and Synthetic Images

Zhang¹,

Chen²

2021

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Facing the image detection of dense small rigid targets, the main bottleneck of convolutional neural network (CNN)-based algorithms is the lack of massive correctly labeled training images. To make up for the lack, this paper proposes an automatic end-to-end synthesis algorithm to generate a huge amount of labeled training samples. The synthetic image set was adopted to train the network progressively and iteratively, realizing the detection of dense small rigid targets based on the CNN and synthetic images. Specifically, the standard images of the target classes and the typical background mages were imported, and the color, brightness, position, orientation, and perspective of real images were simulated by image processing algorithm, creating a sufficiently large initial training set with correctly labeled images. Then, the network was preliminarily trained on this set. After that, a few real images were compiled into the test set. Taking the missed and incorrectly detected target images as inputs, the initial training set was progressively expanded, and then used to iteratively train the network. The results show that our method can automatically generate a training set that fully substitutes manually labeled dataset for network training, eliminating the dependence on massive manually labeled images. The research opens a new way to implement the tasks similar to the detection of dense small rigid targets, and provides a good reference for solving similar problems through deep learning (DL).

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An inexpensive, automated and reproducible method to conduct quality control in nanoparticles

et al. 2020

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Automatic detection of hypoxia in renal tissue stained with HIF-1alpha

Cited by 3 publications

References 29 publications

Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections

Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections

Detection of Dense Small Rigid Targets Based on Convolutional Neural Network and Synthetic Images

An inexpensive, automated and reproducible method to conduct quality control in nanoparticles

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