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Cell Nucleus

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Attention Mask R-CNN with edge refinement algorithm for identifying circulating genetically abnormal cells.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Recent studies have suggested that circulating tumor cells with abnormalities in gene copy numbers in mononuclear cell-enriched peripheral blood samples, such as circulating genetically abnormal cells (CACs), can be used as a non-invasive tool to det...

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues.

Communications biology
Upcoming technologies enable routine collection of highly multiplexed (20-60 channel), subcellular resolution images of mammalian tissues for research and diagnosis. Extracting single cell data from such images requires accurate image segmentation, a...

Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy.

Communications biology
Bioluminescence microscopy is an appealing alternative to fluorescence microscopy, because it does not depend on external illumination, and consequently does neither produce spurious background autofluorescence, nor perturb intrinsically photosensiti...

NuKit: A deep learning platform for fast nucleus segmentation of histopathological images.

Journal of bioinformatics and computational biology
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...

Leukocyte deep learning classification assessment using Shapley additive explanations algorithm.

International journal of laboratory hematology
INTRODUCTION: A peripheral blood smear is a basic test for hematological disease diagnosis. This test is performed manually in many places worldwide, which requires both time and qualified staff. Large laboratories are equipped with digital morpholog...

Multi CNN based automatic detection of mitotic nuclei in breast histopathological images.

Computers in biology and medicine
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 ...

Active mesh and neural network pipeline for cell aggregate segmentation.

Biophysical journal
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...

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)
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...

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...

An Automated Framework for Histopathological Nucleus Segmentation With Deep Attention Integrated Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Clinical management and accurate disease diagnosis are evolving from qualitative stage to the quantitative stage, particularly at the cellular level. However, the manual process of histopathological analysis is lab-intensive and time-consuming. Meanw...