Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT

Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT

(1) Background: Optimal anatomic coverage is important for radiation-dose optimization. We trained and tested (R2.2.4) two (R3-2) deep learning (DL) algorithms on a machine vision tool library platform (Cognex Vision Pro Deep Learning software) to recognize anatomic landmarks and classify chest CT a...

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Bibliographic Details
Main Authors: Parisa Kaviani, Bernardo C. Bizzo, Subba R. Digumarthy, Giridhar Dasegowda, Lina Karout, James Hillis, Nir Neumark, Mannudeep K. Kalra, Keith J. Dreyer
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Diagnostics
Subjects:
deep learning model
chest CT examination
suboptimal chest CT imaging
Online Access:https://www.mdpi.com/2075-4418/12/8/1844

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https://www.mdpi.com/2075-4418/12/8/1844

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