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Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

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Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
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Related Experiment Video

Updated: Oct 26, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Anatomic Point-Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest

Feng Li1, Samuel G Armato2, Roger Engelmann2

  • 1Department of Radiology, MC-2026, The University of Chicago, 5841 S. Maryland Avenue, Chicago, IL, 60637, USA. feng@uchicago.edu.

Journal of Digital Imaging
|July 30, 2021
PubMed
Summary
This summary is machine-generated.

This study developed a reliable chest radiograph (CXR) lung segmentation method using anatomic points. Automated point placement by a neural network showed high accuracy, proving useful for machine learning in radiology.

Keywords:
Anatomic PointsChest RadiographyPoint-Based Lung Zone SegmentationReference StandardU-Net Neural Network

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Chest radiographs (CXRs) are crucial for diagnosing lung conditions.
  • Establishing reliable reference standards for automated analysis is essential for advancing AI in radiology.
  • Anatomic point-based segmentation offers a potential framework for consistent lung zone definition.

Purpose of the Study:

  • To assess the reliability and utility of anatomic point-based lung zone segmentation on CXRs.
  • To evaluate the accuracy of automated point placement for creating lung zones.
  • To develop and test a prototype algorithm for detecting cardiomegaly and pleural effusions using this segmentation.

Main Methods:

  • Two radiologists manually identified five key anatomic points on 200 frontal CXRs.
  • Eight lung zones were automatically generated from these points.
  • A U-Net neural network was trained to automatically place these points on an independent dataset of 379 CXRs.
  • The algorithm's performance in measuring cardiothoracic ratio, diaphragm position, and pleural effusion was evaluated.

Main Results:

  • Manual point identification showed higher variation for obscured points compared to visible points.
  • The automated algorithm successfully measured cardiothoracic ratio, diaphragm position, and pleural effusion.
  • The neural network achieved a mean point placement accuracy of 6.2 mm compared to radiologists.
  • The network correctly identified 95% of radiologist-indicated points with a 3% false-positive rate.

Conclusions:

  • A reliable anatomic point-based lung segmentation method for CXRs has been developed.
  • This method shows utility for establishing reference standards in machine learning applications for radiology.
  • Automated point placement by neural networks is accurate and reliable for lung zone segmentation.