DeepSeeded: Volumetric segmentation of dense cell populations with a cascade of deep neural networks in bacterial biofilm applications
Accurate and automatic segmentation of individual cell instances in microscopy images is a
vital step for quantifying the cellular attributes, which can subsequently lead to new …
vital step for quantifying the cellular attributes, which can subsequently lead to new …
Super resolution-based methodology for self-supervised segmentation of microscopy images
Data-driven Artificial Intelligence (AI)/Machine learning (ML) image analysis approaches
have gained a lot of momentum in analyzing microscopy images in bioengineering …
have gained a lot of momentum in analyzing microscopy images in bioengineering …
Development of deep learning-based mobile application for the identification of Coccidia species in pigs using microscopic images
Coccidiosis is a gastrointestinal parasitic disease caused by different species of Eimeria and
Isospora, poses a significant threat to pig farming, leading to substantial economic losses …
Isospora, poses a significant threat to pig farming, leading to substantial economic losses …
Machine Learning-Assisted Optical Detection of Multilayer Hexagonal Boron Nitride for Enhanced Characterization and Analysis
Biofilms are ubiquitous in aqueous environments, exerting significant influence on diverse
surfaces, including metals prone to microbiologically influenced corrosion (MIC). This …
surfaces, including metals prone to microbiologically influenced corrosion (MIC). This …
Accurate and fast extraction of adhesive cells based on concave points detection and matching
D Liang, YJ Pi, K Hu, YG Cui, Y Huang… - … Journal of Imaging …, 2024 - Wiley Online Library
Precise image segmentation of adhesive cells is a challenge during the cell detection
process for biomedical image analyses. In this article, a new segmentation and extraction …
process for biomedical image analyses. In this article, a new segmentation and extraction …
Bacterial Image Segmentation through Deep Learning Approach
For decades, advances in volume scanning electron microscopy (SEM) have contributed
significantly to increasing large, high-resolution three-dimensional (3D) images, and deep …
significantly to increasing large, high-resolution three-dimensional (3D) images, and deep …
Gaussian model for closed curves
In the case of image processing or understanding, one of the common important tasks is to fit
closed curves (eg, circles, ellipses, etc.) to the underlying image. In higher-dimensional …
closed curves (eg, circles, ellipses, etc.) to the underlying image. In higher-dimensional …
Machine Learning Approach to Estimate Volumetric Quantification of 2D SEM Images of Biofilms
Scanning Electron Microscopy (SEM) is one of the most important imaging techniques to
understand the dynamics of microscale objects. However, SEM images could mostly …
understand the dynamics of microscale objects. However, SEM images could mostly …
Label Efficient Learning for Multi-Label Classification With Self-Supervision
DLB Abeyrathna, G Mudiyanselage - 2024 - search.proquest.com
Deep neural networks, trained with supervised learning algorithms have demonstrated
remarkable performance in analyzing large, labeled datasets. However, obtaining sufficient …
remarkable performance in analyzing large, labeled datasets. However, obtaining sufficient …
Classification of the most common conditionally pathogenic microorganisms on SEM images with YOLO model
VN Gridin, IA Novikov, BR Salem… - 2023 IX International …, 2023 - ieeexplore.ieee.org
A relevant and highly demanded modern medicine problem in many of its areas is the timely
detection and recognition of pathogenic microorganisms and microbial communities in the …
detection and recognition of pathogenic microorganisms and microbial communities in the …