Multi Class Segmentation
Multi Class Segmentation - Related Videos
04:48
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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2024
The present protocol describes an efficient multi-organ segmentation method called Swin-PSAxialNet, which has achieved excellent accuracy compared to previous segmentation methods. The key steps of this procedure include dataset collection, environment configuration, data preprocessing, model training and comparison, and ablation experiments.
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.
11:25
Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
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Cited by 29 •
2014
The reliability of results in metabolomics experiments depends on the effectiveness and reproducibility of the sample preparation. Described is a rigorous and in-depth method that enables extraction of metabolites from biological fluids with the option of subsequently analyzing up to thousands of compounds, or just the compound classes of interest.
11:38
Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
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Cited by 8 •
2017
Segmentation of three-dimensional data from many imaging techniques is a major bottleneck in analysis of complex biological systems. Here, we describe the use of SuRVoS Workbench to semi-automatically segment volumetric data at various length-scales using example datasets from cryo-electron tomography, cryo soft X-ray tomography, and phase contrast X-ray tomography techniques.
07:35
The Multi-group Experiment
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2023
Source: Laboratories of Gary Lewandowski, Dave Strohmetz, and Natalie Ciarocco—Monmouth University A multi-group design is an experimental design that has 3 or more conditions/groups of the same independent variable. This video demonstrates a multi-group experiment that examines how different interethnic ideologies (multiculturalism and color-blind) influence feelings about diversity and actions toward and out-group member. In providing an overview of how a researcher conducts a multi-group...
FAQs
During prediction, the model evaluates image patterns at each pixel or voxel and assigns probabilities to the available classes. The resulting map records the selected category across the image, so adjacent regions can be distinguished according to their predicted labels rather than treated as one undifferentiated signal. This mechanism turns visual structure into a format suitable for measurement.
Predefined classes establish the categories the model can recognize, such as tissues, cells, organelles, or background. They determine how biological regions are separated in the final labeled map and therefore shape the measurements that can be extracted. A class scheme focused on relevant structures allows the analysis to represent the biological organization present in the images.
The unit being labeled depends on the image representation: pixels refer to image locations in two dimensions, whereas voxels represent locations in volumetric data. Using voxel-level labeling allows structures to be separated through an image volume rather than a single plane. This distinction matters when interpreting measurements of biological structures across spatially organized imaging data.
Class probabilities show how the model distributes its prediction among the available categories at each image location. They provide more information than a final label alone because the prediction process retains the category-level outputs used to produce that label. Examining these outputs can help researchers understand how the labeled map was generated before deriving biological measurements.
The workflow begins with images that have been labeled for the categories of interest. A computational model learns patterns from those labeled examples, then predicts class probabilities across a new image or image volume. Those predictions are converted into a labeled map that distinguishes the relevant structures and provides the basis for subsequent biological measurements.
Researchers can use this approach when an image contains several biologically meaningful categories that must be separated for analysis. It supports microscopy studies, tissue characterization, disease assessment, and investigations of cell morphology or spatial organization. The method is especially relevant when analyzing large imaging datasets, where repeated manual identification would make consistent measurement more difficult.
By reducing reliance on manual annotation, the method can make structure identification more standardized across images. Its labeled maps provide a consistent basis for quantifying tissues, cells, organelles, or background, while preserving their spatial arrangement. This supports reproducible biological measurements and helps researchers compare image-derived features across larger collections of microscopy data.
A labeled map can separate biological structures so researchers can measure their presence and distinguish their spatial organization within an image. Depending on the selected categories, the analysis can support characterization of tissues, cells, organelles, and background, as well as studies of cell morphology. These outputs connect image structure with quantitative biological investigation.