Improvement in Disease Diagnosis in Computed Tomography Images by Correlating Organ Volumes with Disease Occurrences in Humans

  • In this paper, we investigate the influence of different disease groups on the size of different 1 anatomical structures. To this end, we first modify and improve an existing anatomical segmentation 2 model. Then, we use this model to segment 104 anatomical structures from computed tomography 3 (CT) scans and compute their volumes from the segmentation. After correlating the results with each 4 other, we find no new significant correlations. After correlating the volume data with known diseases 5 for each case, we find two weak correlations, one of which has not been described before and for 6 which we present a possible explanation.

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Author:Timo van MeegdenburgORCiD, Jens KleesiekORCiD, Jan EggerORCiD, Sören PerreyORCiD
URN:urn:nbn:de:hbz:1010-opus4-42927
DOI:https://doi.org/10.3390/biomedinformatics3030036
ISSN:2673-7426
Parent Title (English):BioMedInformatics
Publisher:MDPI
Document Type:Article
Language:English
Date of Publication (online):2023/12/19
Date of first Publication:2023/07/05
Publishing Institution:Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen
Release Date:2024/01/26
Tag:Convolutional Neural Networks; Correlation; Diseases; Segmentation
Volume:2023
Issue:3 (3)
Page Number:17
First Page:526
Last Page:542
Institutes:Institute / Institut für biologische und chemische Informatik
Licence (German):Creative Commons - Namensnennung
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