Original title: Deep learning model for segmentation of trabecular tissue on CT data of the lumbar spine
Authors: Nagyová, Miriam ; Nohel, Michal
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: This paper focuses on training a deep learning model for vertebral body segmentation of the lumbar spine. The nnU-Net model was trained and tested on a publicly available dataset LumVBCanSeg consisting of 185 lumbar CT scans. Dice coefficient was used to evaluate the accuracy of the trained model. The mean Dice coefficient of the testing dataset was 0.949 with a standard deviation of 0.103. The model was also tested on clinical data containing various abnormalities, such as lytic lesions in multiple myeloma patients and metallic implants. Results were evaluated visually. While the model showed high accuracy on the testing dataset, the results on scans with anomalies showed a decline in accuracy.
Keywords: multiple myeloma; nnU-Net; osteolytic lesions; segmentation
Host item entry: Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers, ISBN 978-80-214-6230-4, ISSN 2788-1334

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: https://hdl.handle.net/11012/249286

Permalink: http://www.nusl.cz/ntk/nusl-622590


The record appears in these collections:
Universities and colleges > Public universities > Brno University of Technology
Conference materials > Papers