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Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

953
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
953

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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GOARS: Generalized organ-at-risk segmentation utilizing hierarchical learning architecture and multi-dimensional

Xuezheng Sun1, Tao Wan1, Jiankun Xu2

  • 1School of Biomedical Science and Medical Engineering, Beihang University, Beijing, 100083, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China.

Computers in Biology and Medicine
|November 27, 2025
PubMed
Summary

A new generalized organs-at-risk (OARs) segmentation method (GOARS) improves radiotherapy planning accuracy. This automated solution precisely segments both large and small organs, overcoming limitations of current deep learning approaches.

Keywords:
Feature aggregationHierarchical learningMulti-organ segmentationOrgan-at-riskRadiotherapy

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Deep Learning

Background:

  • Accurate segmentation of organs-at-risk (OARs) is crucial for effective radiotherapy.
  • Manual delineation is time-consuming and prone to inter-observer variability.
  • Current automated methods struggle with anatomical diversity, low contrast, and class imbalance.

Purpose of the Study:

  • To develop a generalized and robust automated method for OAR segmentation.
  • To address the challenges faced by existing deep learning segmentation techniques.
  • To improve the precision and reliability of radiotherapy planning.

Main Methods:

  • Proposed a generalized OAR segmentation method (GOARS) using a hierarchical learning architecture.
  • Implemented a coarse-to-fine framework for segmenting both large and small organs.
  • Integrated adaptive ROI extraction and a dual 2D/3D network for multi-dimensional feature aggregation.

Main Results:

  • GOARS demonstrated robust performance across three independent datasets.
  • The method accurately segmented OARs with high precision.
  • Successfully handled anatomical variability and challenges like class imbalance.

Conclusions:

  • GOARS offers a unified and effective solution for OAR segmentation.
  • The method enhances the accuracy and reliability of radiotherapy planning.
  • This approach has the potential to significantly advance clinical radiotherapy practices.