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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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The ASC Module: A GPU Memory-Efficient, Physiology-Aware Approach for Improving Segmentation Accuracy on Poorly

Zuoyuan Zhao1, Toru Higaki1, Yanlei Gu1

  • 1Informatics and Data Science Program, Graduate School of Advance Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan.

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|September 27, 2025
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Summary
This summary is machine-generated.

A new Automatic Spatial Contrast (ASC) Module improves AI-based aorta segmentation on CT scans, especially for poorly contrasted images. This enhances surgical planning for aging populations facing medical resource limitations.

Keywords:
CT imagesGPU memory-efficientdeep learningimage segmentationpoor/non-contrasted CTspatial structure

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Aging populations, like Japan's, risk inadequate medical resources.
  • Accurate pre-surgical aorta localization via CT is crucial.
  • Current AI models struggle with poorly contrasted CT images.

Purpose of the Study:

  • To enhance AI-driven aorta segmentation on low-contrast CT scans.
  • To improve pre-surgical planning accuracy.
  • To address limitations of existing semantic segmentation models.

Main Methods:

  • Developed an Automatic Spatial Contrast (ASC) Module.
  • Integrated ASC Module with UNet, Attention UNet, TransUNet, and Swin-UNet.
  • Evaluated model performance on aorta segmentation in CT images.

Main Results:

  • Significant improvements in Intersection-over-Union (IoU) up to 24.84%.
  • Substantial gains in Dice Similarity Coefficient (DSC) up to 28.13%.
  • Minimal increase in GPU memory usage compared to baseline models.

Conclusions:

  • The ASC Module effectively improves aorta segmentation accuracy on challenging CT images.
  • This AI enhancement aids pre-surgical planning, particularly for at-risk populations.
  • The method offers a practical solution with efficient resource utilization.