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Imaging Studies for Cardiovascular System V: CT01:28

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...

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Non-Contrast Brain CT Images Segmentation Enhancement: Lightweight Pre-Processing Model for Ultra-Early Ischemic

Aleksei Samarin1, Alexander Savelev2, Aleksei Toropov1

  • 1Higher School of Digital Culture, ITMO University, St. Petersburg 197101, Russia.

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|October 28, 2025
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Summary

This study introduces a novel deep learning method for precise segmentation of ultra-early ischemic stroke lesions in CT scans. The approach enhances image clarity without artifacts, improving early diagnosis and treatment planning for ischemic stroke.

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computer tomography snapshotsimage preprocessingimage segmentationischemic stroke recognition

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Area of Science:

  • Neurology
  • Radiology
  • Artificial Intelligence

Background:

  • Accurate identification of ultra-early ischemic stroke lesions on non-contrast CT is critical for timely intervention.
  • Existing preprocessing methods can introduce artifacts, hindering the detection of subtle, early stroke signs.

Purpose of the Study:

  • To develop and validate a deep learning methodology for segmenting ultra-early ischemic core and penumbra in non-contrast CT scans.
  • To introduce an artifact-free preprocessing model enhancing image clarity for subtle ischemic lesion detection.

Main Methods:

  • A lightweight deep learning preprocessing model using convolutional filtering and trainable parameters was developed.
  • A novel trainable linear combination of pretrained image filters was incorporated into the pipeline.
  • The model was trained and evaluated on a public dataset of 112 acute ischemic stroke non-contrast CT scans.

Main Results:

  • The proposed model achieved high segmentation accuracy for ultra-early ischemic regions.
  • Performance metrics surpassed existing methodologies for ultra-early stroke lesion detection.
  • Rigorous validation on test subsets confirmed the model's effectiveness.

Conclusions:

  • The developed deep learning approach offers a precise and artifact-free method for segmenting ultra-early ischemic stroke lesions.
  • This methodology shows significant potential for improving early diagnosis and treatment planning in acute ischemic stroke cases.