AIMC Topic: Cell Nucleus

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Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches.

PLoS computational biology Sep 6, 2023
Accurate prediction of nucleic binding residues is essential for the understanding of transcription and translation processes. Integration of feature- and template-based strategies could improve the prediction of these key residues in proteins. Never...
Algorithms Cell Nucleus Nucleic Acids Walking Deep Learning
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An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer.

BMC cancer Aug 17, 2023
BACKGROUND AND OBJECTIVE: In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based on artifi...
Hydrolases Cell Nucleus Prognosis Artificial Intelligence Tumor Microenvironment Colorectal Neoplasms Humans
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Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality.

EBioMedicine Jul 25, 2023
BACKGROUND: Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable patient strat...
Adenocarcinoma Colonic Neoplasms Genomics Cell Nucleus Deep Learning Humans
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Protocol for automated multivariate quantitative-image-based cytometry analysis by fluorescence microscopy of asynchronous adherent cells.

STAR protocols Jul 13, 2023
Here, we present a protocol for multivariate quantitative-image-based cytometry (QIBC) analysis by fluorescence microscopy of asynchronous adherent cells. We describe steps for the preparation, treatment, and fixation of cells, sample staining, and i...
Microscopy, Fluorescence Cell Nucleus Artificial Intelligence Data Visualization Histological Techniques
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Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology.

PloS one Jun 23, 2023
Robust semantic segmentation of tumour micro-environment is one of the major open challenges in machine learning enabled computational pathology. Though deep learning based systems have made significant progress, their task agnostic data driven appro...
Image Processing, Computer-Assisted Cell Nucleus Deep Learning Water Machine Learning
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FiNuTyper: Design and validation of an automated deep learning-based platform for simultaneous fiber and nucleus type analysis in human skeletal muscle.

Acta physiologica (Oxford, England) May 8, 2023
AIM: While manual quantification is still considered the gold standard for skeletal muscle histological analysis, it is time-consuming and prone to investigator bias. To address this challenge, we assembled an automated image analysis pipeline, FiNuT...
Satellite Cells, Skeletal Muscle Cell Nucleus Muscle Fibers, Skeletal Muscle, Skeletal Deep Learning Humans
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Which Pixel to Annotate: A Label-Efficient Nuclei Segmentation Framework.

IEEE transactions on medical imaging Apr 3, 2023
Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset o...
Cell Nucleus Supervised Machine Learning Neural Networks, Computer
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Active mesh and neural network pipeline for cell aggregate segmentation.

Biophysical journal Mar 30, 2023
Segmenting cells within cellular aggregates in 3D is a growing challenge in cell biology due to improvements in capacity and accuracy of microscopy techniques. Here, we describe a pipeline to segment images of cell aggregates in 3D. The pipeline comb...
Animals Mice Image Processing, Computer-Assisted Neural Networks, Computer Cell Nucleus Microscopy, Fluorescence
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Multi CNN based automatic detection of mitotic nuclei in breast histopathological images.

Computers in biology and medicine Mar 22, 2023
In breast cancer diagnosis, the number of mitotic cells in a specific area is an important measure. It indicates how far the tumour has spread, which has consequences for forecasting the aggressiveness of cancer. Mitosis counting is a time-consuming ...
Mitosis Cell Nucleus Humans Breast Neoplasms Breast Female Neural Networks, Computer
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NuKit: A deep learning platform for fast nucleus segmentation of histopathological images.

Journal of bioinformatics and computational biology Mar 11, 2023
Nucleus segmentation represents the initial step for histopathological image analysis pipelines, and it remains a challenge in many quantitative analysis methods in terms of accuracy and speed. Recently, deep learning nucleus segmentation methods hav...
Cell Nucleus Image Processing, Computer-Assisted Deep Learning
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