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Cell Nucleus

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Quantitative analysis of the dexamethasone side effect on human-derived young and aged skeletal muscle by myotube and nuclei segmentation using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Skeletal muscle cells (skMCs) combine together to create long, multi-nucleated structures called myotubes. By studying the size, length, and number of nuclei in these myotubes, we can gain a deeper understanding of skeletal muscle develop...

A CNN-GNN Approach for Polarity Vectors Prediction in 3D Microscopy Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The polarity between nuclei and Golgi is an important aspect of cellular division, migration and signaling. For example, nucleus-Golgi polarity significantly impacts angiogenesis, the physiological process in which new blood vessels develop from pre-...

Effect of learning parameters on the performance of the U-Net architecture for cell nuclei segmentation from microscopic cell images.

Microscopy (Oxford, England)
Nuclei segmentation of cells is the preliminary and essential step of pathological image analysis. However, robust and accurate cell nuclei segmentation is challenging due to the enormous variability of staining, cell sizes, morphologies, cell adhesi...

A Cascaded Deep Learning Framework for Segmentation of Nuclei in Digital Histology Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Accurate segmentation of nuclei is an essential step in analysis of digital histology images for diagnostic and prognostic applications. Despite recent advances in automated frameworks for nuclei segmentation, this task is still challenging. Specific...

Context-aware learning for cancer cell nucleus recognition in pathology images.

Bioinformatics (Oxford, England)
MOTIVATION: Nucleus identification supports many quantitative analysis studies that rely on nuclei positions or categories. Contextual information in pathology images refers to information near the to-be-recognized cell, which can be very helpful for...

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
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 ce...

Dual Encoder Attention U-net for Nuclei Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Nuclei segmentation in whole slide images (WSIs) stained with Hematoxylin and Eosin (H&E) dye, is a key step in computational pathology which aims to automate the laborious process of manual counting and segmentation. Nuclei segmentation is a challen...

An enhanced loss function simplifies the deep learning model for characterizing the 3D organoid models.

Bioinformatics (Oxford, England)
MOTIVATION: Organization of the organoid models, imaged in 3D with a confocal microscope, is an essential morphometric index to assess responses to stress or therapeutic targets. In fact, differentiating malignant and normal cells is often difficult ...

DM3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism.

Nucleic acids research
Subcellular localization of messenger RNAs (mRNAs), as a prevalent mechanism, gives precise and efficient control for the translation process. There is mounting evidence for the important roles of this process in a variety of cellular events. Computa...

mRNALoc: a novel machine-learning based in-silico tool to predict mRNA subcellular localization.

Nucleic acids research
Recent evidences suggest that the localization of mRNAs near the subcellular compartment of the translated proteins is a more robust cellular tool, which optimizes protein expression, post-transcriptionally. Retention of mRNA in the nucleus can regul...