Machine learning for lung image analysis: towards the automatic quantification of airway diseases

Antonio Garcia-Uceda Juarez

Machine learning for lung image analysis: towards the automatic quantification of airway diseases

Antonio Garcia-Uceda Juarez

Radiology & Nuclear Medicine

Research output: Types of Thesis › Doctoral Thesis › Internal

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Abstract

In this thesis, we developed automatic image processing methods to segment the bronchial tree from chest CT scans and subsequently extracted airway measurements. Moreover, we applied these automated measurements as quantitative biomarkers to analyze CT scans for various lung diseases.

Original language	English
Awarding Institution	Erasmus University Rotterdam
Supervisors/Advisors	de Bruijne, Marleen, Supervisor Tiddens, H.A.W.M., Supervisor
Award date	26 Oct 2022
Place of Publication	Rotterdam
Print ISBNs	978-94-6458-507-0
Publication status	Published - 26 Oct 2022

Bibliographical note

This research was funded by the Innovative Medicines Initiative (IMI) (n. 115721).

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final dissertation Juarez, Antonio Final published version, 47.2 MB
embargo version Juarez, AntonioOther version, 37.3 MB

Cite this

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title = "Machine learning for lung image analysis: towards the automatic quantification of airway diseases",

abstract = "In this thesis, we developed automatic image processing methods to segment the bronchial tree from chest CT scans and subsequently extracted airway measurements. Moreover, we applied these automated measurements as quantitative biomarkers to analyze CT scans for various lung diseases.",

author = "{Garcia-Uceda Juarez}, Antonio",

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N2 - In this thesis, we developed automatic image processing methods to segment the bronchial tree from chest CT scans and subsequently extracted airway measurements. Moreover, we applied these automated measurements as quantitative biomarkers to analyze CT scans for various lung diseases.

AB - In this thesis, we developed automatic image processing methods to segment the bronchial tree from chest CT scans and subsequently extracted airway measurements. Moreover, we applied these automated measurements as quantitative biomarkers to analyze CT scans for various lung diseases.

M3 - Doctoral Thesis

SN - 978-94-6458-507-0

CY - Rotterdam

ER -

Machine learning for lung image analysis: towards the automatic quantification of airway diseases

Abstract

Bibliographical note

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