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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

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Pulmonary embolism: computer-aided detection at multidetector row spiral computed tomography.

U Joseph Schoepf¹, Alex C Schneider, Marco Das

¹Department of Radiology, Medical University of South Carolina, Charleston, SC 29425, USA. schoepf@musc.edu

Journal of Thoracic Imaging

|November 29, 2007

Summary

A computer-aided detection (CAD) tool effectively identifies segmental and subsegmental pulmonary emboli on CT scans. This technology shows high sensitivity and specificity, potentially improving diagnostic accuracy for pulmonary embolism.

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Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

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Published on: October 13, 2023

Area of Science:

Radiology
Medical Imaging
Pulmonary Medicine

Background:

Pulmonary embolism (PE) diagnosis relies on computed tomographic angiography (CTA).
Accurate detection of peripheral emboli (segmental and subsegmental) remains challenging.
Computer-aided detection (CAD) tools offer potential for automated analysis.

Purpose of the Study:

To evaluate the feasibility and performance of a CAD tool for detecting segmental and subsegmental pulmonary emboli.
To assess the CAD system's accuracy compared to expert radiologist consensus.

Main Methods:

A CAD tool (ImageChecker CT) was tested on multidetector-row CT studies from 23 patients with PE and 13 without PE.
Studies utilized 16 x 1 mm collimation and 1.25 mm reconstructed section width.
Performance was assessed against a consensus reading by three radiologists.

Main Results:

The CAD system correctly identified all 23 patients with PE.
Sensitivity for segmental emboli was 92% and for subsegmental emboli was 90%.
Overall specificity was 89.9%, with a positive predictive value of 63.2% and negative predictive value of 97.7%.

Conclusions:

CAD for detecting segmental and subsegmental pulmonary emboli is feasible using 1-mm multidetector-row CT.
CAD tools may enhance diagnostic accuracy and reduce interpretation time for CTA in PE detection.
Automated detection could increase the acceptance of CTA as a first-line diagnostic test for suspected PE.