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Microscopy

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Context-aware learning for cancer cell nucleus recognition in pathology images.

Bioinformatics (Oxford, England)
MOTIVATION: Nucleus identification supports many quantitative analysis studies that rely on nuclei positions or categories. Contextual information in pathology images refers to information near the to-be-recognized cell, which can be very helpful for...

The Delta Robot-A long travel nano-positioning stage for scanning x-ray microscopy.

The Review of scientific instruments
A new stage design concept, the Delta Robot, is presented, which is a parallel kinematic design for scanning x-ray microscopy applications. The stage employs three orthogonal voice coils, which actuate parallelogram flexures. The design has a 3 mm tr...

In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment.

Journal of biomedical optics
SIGNIFICANCE: There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, comme...

Morphological components detection for super-depth-of-field bio-micrograph based on deep learning.

Microscopy (Oxford, England)
Accompanied with the clinical routine examination demand increase sharply, the efficiency and accuracy are the first priority. However, automatic classification and localization of cells in microscopic images in super depth of Field (SDoF) system rem...

Automated Microscopy Image Segmentation and Analysis with Machine Learning.

Methods in molecular biology (Clifton, N.J.)
The development of automated quantitative image analysis pipelines requires thoughtful considerations to extract meaningful information. Commonly, extraction rules for quantitative parameters are defined and agreed beforehand to ensure repeatability ...

Deep learning multi-shot 3D localization microscopy using hybrid optical-electronic computing.

Optics letters
Current 3D localization microscopy approaches are fundamentally limited in their ability to image thick, densely labeled specimens. Here, we introduce a hybrid optical-electronic computing approach that jointly optimizes an optical encoder (a set of ...

Automatic improvement of deep learning-based cell segmentation in time-lapse microscopy by neural architecture search.

Bioinformatics (Oxford, England)
MOTIVATION: Live cell segmentation is a crucial step in biological image analysis and is also a challenging task because time-lapse microscopy cell sequences usually exhibit complex spatial structures and complicated temporal behaviors. In recent yea...

Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set.

Blood
Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times e...

CellProfiler Analyst 3.0: accessible data exploration and machine learning for image analysis.

Bioinformatics (Oxford, England)
SUMMARY: Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of...

Deep Phenotypic Cell Classification using Capsule Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Recent developments in ultra-high-throughput microscopy have created a new generation of cell classification methodologies focused solely on image-based cell phenotypes. These image-based analyses enable morphological profiling and screening of thous...