AIMC Topic: Cell Nucleus

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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 ...

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...

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...

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...

FEEDNet: a feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis.

Physics in medicine and biology
Automated cell nuclei segmentation is vital for the histopathological diagnosis of cancer. However, nuclei segmentation from 'hematoxylin and eosin' (HE) stained 'whole slide images' (WSIs) remains a challenge due to noise-induced intensity variation...

Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database.

Journal of digital imaging
Machine learning has been recently used especially in the medical field. In the diagnosis of serious diseases such as cancer, deep learning techniques can be used to reduce the workload of experts and to produce quick solutions. The nuclei found in t...

Usability of deep learning pipelines for 3D nuclei identification with Stardist and Cellpose.

Cells & development
Segmentation of 3D images to identify cells and their molecular outputs can be difficult and tedious. Machine learning algorithms provide a promising alternative to manual analysis as emerging 3D image processing technology can save considerable time...

Detecting and Classifying Nuclei Using Multi-Scale Fully Convolutional Network.

Journal of computational biology : a journal of computational molecular cell biology
The detection and classification of nuclei play an important role in the histopathological analysis. It aims to find out the distribution of nuclei in the histopathology images for the next step of analysis and research. However, it is very challengi...

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...