Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan

Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan

Background: Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Most software labels MSU as green and calcium as blue. There a...

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Bibliographic Details
Main Authors: Shahriar Faghani, Rhodes G. Nicholas, Soham Patel, Francis I. Baffour, Mana Moassefi, Pouria Rouzrokh, Bardia Khosravi, Garret M. Powell, Shuai Leng, Katrina N. Glazebrook, Bradley J. Erickson, Christin A. Tiegs-Heiden
Format: Article
Language:English
Published: Elsevier 2024-03-01
Series:Research in Diagnostic and Interventional Imaging
Subjects:
Gout
Dual-energy CT
Deep learning
Segmentation
Convolutional neural network
Vision transformers
Online Access:http://www.sciencedirect.com/science/article/pii/S277265252400005X

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http://www.sciencedirect.com/science/article/pii/S277265252400005X

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