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Imaging Studies for Cardiovascular System III: X-Ray01:20

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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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NCT-CXR: Enhancing Pulmonary Abnormality Segmentation on Chest X-Rays Using Improved Coordinate Geometric

Abu Salam1,2,3, Pulung Nurtantio Andono1, Purwanto1

  • 1Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang 50131, Indonesia.

Journal of Imaging
|June 25, 2025
PubMed
Summary
This summary is machine-generated.

NCT-CXR enhances pulmonary abnormality segmentation in chest X-rays using anatomically constrained data augmentation and expert-guided annotation refinement. This improves precision for conditions like pneumothorax, offering a reliable deep learning solution for radiology.

Keywords:
chest X-raycoordinate transformationdata augmentationgeometric transformationmulti-label segmentationpulmonary abnormalities

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computer Vision

Background:

  • Medical image segmentation, particularly for chest X-rays (CXRs), faces challenges like class imbalance and inconsistent annotations.
  • Accurate identification of pathological regions in CXRs is crucial for diagnosis and treatment planning.
  • Existing methods often struggle with the nuances of anatomical variations and subtle abnormalities.

Purpose of the Study:

  • To develop NCT-CXR, a novel framework for precise and clinically reliable pulmonary abnormality segmentation in CXRs.
  • To improve deep learning model performance by combining anatomically constrained data augmentation with expert-guided annotation refinement.
  • To address label noise in large CXR datasets and enhance segmentation accuracy for thoracic conditions.

Main Methods:

  • Implemented NCT-CXR framework utilizing discrete-angle rotations (±5°, ±10°) and intensity-based augmentations for data enrichment.
  • Employed a clinically validated annotation refinement pipeline with OncoDocAI for generating high-quality, multi-label pixel-level segmentation masks.
  • Selected YOLOv8 as the segmentation backbone for its efficiency, speed, and spatial accuracy.
  • Utilized NIH Chest X-ray dataset for training and evaluation.

Main Results:

  • NCT-CXR significantly improved segmentation precision, notably for pneumothorax (achieving 0.829 and 0.804 Dice scores with ±5° and ±10° rotations, respectively).
  • Statistical analysis (Kruskal-Wallis, Nemenyi) confirmed the superiority of discrete-angle augmentation over mixed strategies (p < 0.014).
  • The refined annotations and augmentation strategy led to more robust and accurate segmentation masks for nine thoracic conditions.

Conclusions:

  • Clinically constrained data augmentation and high-quality annotation are vital for robust medical image segmentation models.
  • NCT-CXR provides a practical and high-performance solution for integrating deep learning into radiological workflows.
  • The framework demonstrates potential for enhancing diagnostic accuracy and efficiency in CXR analysis.