AIMC Topic: Cell Nucleus
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Convolutional neural network-based regression analysis to predict subnuclear chromatin organization from two-dimensional optical scattering signals.
Journal of biomedical optics
Aug 28, 2024
SIGNIFICANCE: Azimuth-resolved optical scattering signals obtained from cell nuclei are sensitive to changes in their internal refractive index profile. These two-dimensional signals can therefore offer significant insights into chromatin organizatio...
Utilizing convolutional neural networks for discriminating cancer and stromal cells in three-dimensional cell culture images with nuclei counterstain.
Journal of biomedical optics
Aug 24, 2024
SIGNIFICANCE: Accurate cell segmentation and classification in three-dimensional (3D) images are vital for studying live cell behavior and drug responses in 3D tissue culture. Evaluating diverse cell populations in 3D cell culture over time necessita...
aiSEGcell: User-friendly deep learning-based segmentation of nuclei in transmitted light images.
PLoS computational biology
Aug 23, 2024
Segmentation is required to quantify cellular structures in microscopic images. This typically requires their fluorescent labeling. Convolutional neural networks (CNNs) can detect these structures also in only transmitted light images. This eliminate...
An Automated Framework for Histopathological Nucleus Segmentation With Deep Attention Integrated Networks.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
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...
A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context.
Development (Cambridge, England)
Jul 18, 2024
We present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs. We have developed an approach for ground truth generation and iterative training of 3D nuclear segmentation...
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
Jun 3, 2024
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
May 14, 2024
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
May 11, 2024
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
Apr 2, 2024
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
Mar 16, 2024
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...
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