AIMC Topic: Cell Nucleus
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Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images.
SLAS discovery : advancing life sciences R & D
Jun 24, 2021
Advances in microscopy have increased output data volumes, and powerful image analysis methods are required to match. In particular, finding and characterizing nuclei from microscopy images, a core cytometry task, remains difficult to automate. While...
SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images.
Medical image analysis
Jun 21, 2021
The detection of nuclei and cells in histology images is of great value in both clinical practice and pathological studies. However, multiple reasons such as morphological variations of nuclei or cells make it a challenging task where conventional ob...
Active Subdivision Surfaces for the Semiautomatic Segmentation of Biomedical Volumes.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Jun 21, 2021
We present a new family of active surfaces for the semiautomatic segmentation of volumetric objects in 3D biomedical images. We represent our deformable model by a subdivision surface encoded by a small set of control points and generated through a g...
Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples.
Journal of microscopy
Jun 15, 2021
Identifying nuclei is a standard first step when analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or fluorescent transgene expression localised to the nucleus. However, imaging techniques that do not u...
MDC-net: A new convolutional neural network for nucleus segmentation in histopathology images with distance maps and contour information.
Computers in biology and medicine
Jun 10, 2021
Accurate segmentation of nuclei in digital pathology images can assist doctors in diagnosing diseases and evaluating subsequent treatments. Manual segmentation of nuclei from pathology images is time-consuming because of the large number of nuclei an...
Spatially Constrained Context-Aware Hierarchical Deep Correlation Filters for Nucleus Detection in Histology Images.
Medical image analysis
May 24, 2021
Nucleus detection in histology images is a fundamental step for cellular-level analysis in computational pathology. In clinical practice, quantitative nuclear morphology can be used for diagnostic decision making, prognostic stratification, and treat...
DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set.
Scientific data
May 20, 2021
Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Though histologically DLBCL shows varying morphologies, no morphologic features have been consistently demonstrated to correlate with prognosis. We present a morphologic a...
The in vitro micronucleus assay using imaging flow cytometry and deep learning.
NPJ systems biology and applications
May 18, 2021
The in vitro micronucleus (MN) assay is a well-established assay for quantification of DNA damage, and is required by regulatory bodies worldwide to screen chemicals for genetic toxicity. The MN assay is performed in two variations: scoring MN in cyt...
Generalized Fixation Invariant Nuclei Detection Through Domain Adaptation Based Deep Learning.
IEEE journal of biomedical and health informatics
May 11, 2021
Nucleus detection is a fundamental task in histological image analysis and an important tool for many follow up analyses. It is known that sample preparation and scanning procedure of histological slides introduce a great amount of variability to the...
A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks.
Scientific reports
Apr 13, 2021
Breast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time...
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