AIMC Topic: Cell Nucleus

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A Review of Nuclei Detection and Segmentation on Microscopy Images Using Deep Learning With Applications to Unbiased Stereology Counting.

IEEE transactions on neural networks and learning systems
The detection and segmentation of stained cells and nuclei are essential prerequisites for subsequent quantitative research for many diseases. Recently, deep learning has shown strong performance in many computer vision problems, including solutions ...

Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology.

Computers in biology and medicine
Segmentation and classification of large numbers of instances, such as cell nuclei, are crucial tasks in digital pathology for accurate diagnosis. However, the availability of high-quality datasets for deep learning methods is often limited due to th...

Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinic...

Comprehensive data analysis of white blood cells with classification and segmentation by using deep learning approaches.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Deep learning approaches have frequently been used in the classification and segmentation of human peripheral blood cells. The common feature of previous studies was that they used more than one dataset, but used them separately. No study has been fo...

CellViT: Vision Transformers for precise cell segmentation and classification.

Medical image analysis
Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapp...

NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.

Scientific data
In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior s...

Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy.

Scientific data
Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Sciences and Deep Learning. This dataset encompasses three i...

Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue.

Scientific reports
The availability of target cells expressing the HIV receptors CD4 and CCR5 in genital tissue is a critical determinant of HIV susceptibility during sexual transmission. Quantification of immune cells in genital tissue is therefore an important outcom...

Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images.

Nature communications
Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of me...

Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Immunohistochemistry (IHC) is a well-established and commonly used staining method for clinical diagnosis and biomedical research. In most IHC images, the target protein is conjugated with a specific antibody and stained using diaminobenzidine (DAB),...