Autoren:	Müller, Lukas Tibyampansha, Dativa Mildenberger, Peter Panholzer, Torsten Jungmann, Florian Halfmann, Moritz C.
Titel:	Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans
Online-Publikationsdatum:	19-Dez-2023
Erscheinungsdatum:	2023
Sprache des Dokuments:	Englisch
Zusammenfassung/Abstract:	Purpose Kidney volume is important in the management of renal diseases. Unfortunately, the currently available, semi-automated kidney volume determination is time-consuming and prone to errors. Recent advances in its automation are promising but mostly require contrast-enhanced computed tomography (CT) scans. This study aimed at establishing an automated estimation of kidney volume in non-contrast, low-dose CT scans of patients with suspected urolithiasis. Methods The kidney segmentation process was automated with 2D Convolutional Neural Network (CNN) models trained on manually segmented 2D transverse images extracted from low-dose, unenhanced CT scans of 210 patients. The models’ segmentation accuracy was assessed using Dice Similarity Coefficient (DSC), for the overlap with manually-generated masks on a set of images not used in the training. Next, the models were applied to 22 previously unseen cases to segment kidney regions. The volume of each kidney was calculated from the product of voxel number and their volume in each segmented mask. Kidney volume results were then validated against results semi-automatically obtained by radiologists. Results The CNN-enabled kidney volume estimation took a mean of 32 s for both kidneys in a CT scan with an average of 1026 slices. The DSC was 0.91 and 0.86 and for left and right kidneys, respectively. Inter-rater variability had consistencies of ICC = 0.89 (right), 0.92 (left), and absolute agreements of ICC = 0.89 (right), 0.93 (left) between the CNN-enabled and semi-automated volume estimations. Conclusion In our work, we demonstrated that CNN-enabled kidney volume estimation is feasible and highly reproducible in low-dose, non-enhanced CT scans. Automatic segmentation can thereby quantitatively enhance radiological reports.
DDC-Sachgruppe:	610 Medizin 610 Medical sciences
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 04 Medizin
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-9820
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Nutzungsrechte:	CC BY
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by/4.0/
Zeitschrift:	BMC medical imaging 23
Seitenzahl oder Artikelnummer:	187
Verlag:	BioMed Central
Verlagsort:	London
Erscheinungsdatum:	2023
ISSN:	1471-2342
DOI der Originalveröffentlichung:	10.1186/s12880-023-01142-y
Enthalten in den Sammlungen:	DFG-491381577-G