Deep Learning Segmentation
Deep Learning Segmentation - Related Videos
10:25
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Cited by 54 •
2022
This is a method for training a multi-slice U-Net for multi-class segmentation of cryo-electron tomograms using a portion of one tomogram as a training input. We describe how to infer this network to other tomograms and how to extract segmentations for further analyses, such as subtomogram averaging and filament tracing.
04:48
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Cited by 4 •
2022
An object segmentation protocol for orbital computed tomography (CT) images is introduced. The methods of labeling the ground truth of orbital structures by using super-resolution, extracting the volume of interest from CT images, and modeling multi-label segmentation using 2D sequential U-Net for orbital CT images are explained for supervised learning.
08:20
Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
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Cited by 7 •
2023
Worldwide medical blood parasites were automatically screened using simple steps on a low-code AI platform. The prospective diagnosis of blood films was improved by using an object detection and classification method in a hybrid deep learning model. The collaboration of active monitoring and well-trained models helps to identify hotspots of trypanosome transmission.
04:17
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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2024
We present a protocol that combines recombinase polymerase amplification with a CRISPR/Cas12a system for trace detection of DNA viruses and builds portable smartphone microscopy with an artificial intelligence-assisted classification for point-of-care DNA virus detection.
09:47
DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
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Cited by 8 •
2023
DeepOmicsAE is a workflow centered on the application of a deep learning method (i.e., an autoencoder) to reduce the dimensionality of multi-omics data, providing a foundation for predictive models and signaling modules representing multiple layers of omics data.
FAQs
The encoder extracts visual features from microscopy images, while the decoder uses those features to produce a spatially organized output marking structures of interest. This arrangement enables the model to delineate cells, nuclei, tissues, or other targets at the pixel or region level. The resulting mask becomes a quantitative representation that can be analyzed rather than only viewed.
Annotated microscopy images provide the examples needed for learning. They show the network which visual patterns correspond to biologically meaningful labels, allowing it to connect image features with structures such as cells, nuclei, or tissues. The learned relationship can then be applied across image datasets, reducing the need to delineate every object manually and improving consistency between analyses.
Segmentation masks convert image content into explicitly delineated regions. Once structures are represented this way, researchers can count objects, measure morphology, examine spatial organization, and assign phenotypic categories across many images. The mask therefore links visual recognition to quantitative biology, making comparisons across large datasets more practical than relying on unaided inspection alone.
By applying learned image analysis consistently, the method can process images that would be difficult to evaluate reliably by eye. This is especially useful when datasets contain many images or when researchers need comparable measurements across samples. Automation reduces manual annotation demands while supporting more consistent assessment of biological structures.
A practical workflow begins with annotated microscopy images, which provide the examples for model learning. The trained network then processes image data and produces segmentation masks for the structures represented in those examples. Researchers can use the masks for downstream counting, morphology measurement, spatial analysis, or phenotyping across the dataset.
The approach is suited to targets that can be delineated as meaningful image structures, including cells, nuclei, and tissues. Selecting among these targets depends on the biological question: cell masks can support counting, nuclear masks can support morphology measurements, and tissue-level regions can support spatial analysis of organization.
In biology, Deep Learning Segmentation connects image-based structure detection with questions about organization and phenotype. By generating consistent masks across samples, it can help researchers compare cell or tissue morphology, spatial relationships, and phenotypic patterns, including changes associated with disease. Its value lies in turning complex microscopy observations into measurements suitable for systematic study.