prikaz prve stranice dokumenta Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network
  Download
PDF 5.49 MB
Scientific paper - Original scientific paper
Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network
Applied Sciences, 14 (2024), 22; 10343. https://doi.org/10.3390/app142210343
Stojšić, Kristian; Miletić Rigo, Dina; Jurković, Slaven

Cite this document

Stojšić, K., Miletić Rigo, D. & Jurković, S. (2024). Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network. Applied Sciences, 14. (22). doi: 10.3390/app142210343

Stojšić, Kristian, et al. "Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network." Applied Sciences, vol. 14, no. 22, 2024. https://doi.org/10.3390/app142210343

Stojšić, Kristian, Dina Miletić Rigo and Slaven Jurković. "Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network." Applied Sciences 14, no. 22 (2024). https://doi.org/10.3390/app142210343

Stojšić, K., Miletić Rigo, D. and Jurković, S. (2024) 'Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network', Applied Sciences, 14(22). doi: 10.3390/app142210343

Stojšić K, Miletić Rigo D, Jurković S. Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network. Applied Sciences [Internet]. 2024 November 11 [cited 2024 November 14];14(22). doi: 10.3390/app142210343

K. Stojšić, D. Miletić Rigo and S. Jurković, "Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network", Applied Sciences, vol. 14, no. 22, November 2024. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:184:946958. [Accessed: 14 November 2024]

Please login to the repository to save this object to your list.
Metadata
Title (english)Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network
AuthorKristian Stojšić
AuthorDina Miletić Rigo
AuthorSlaven Jurković
Author's institutionUniversity of Rijeka
Faculty of Medicine
(Department of Medical Physics and Biophysics)
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Physics
Biophysics and Medical Physics
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Clinical Medical Sciences
Radiology
Abstract (english)
Vertebral bone quality (VBQ) is a promising new method that can improve screening for osteoporosis. The drawback of the current method is that it requires manual determination of the regions of interest (ROIs) of vertebrae and cerebrospinal fluid (CSF) by a radiologist. In this work, an automatic method for determining the VBQ is proposed, in which the ROIs are obtained using a trained neural network model. A large, publicly available dataset of sagittal lumbar spine MRI images with ground truth segmentations was used to train a BRAU-Net++ hybrid CNN–transformer neural network. The performance of the trained model was evaluated using the dice similarity coefficient (DSC), accuracy, precision, recall and intersection-over-union (IoU) metrics. The trained model performed similarly to state-of-the-art lumbar spine segmentation models, with an average DSC value of 0.914 ± 0.007 for the vertebrae and 0.902 for the spinal canal. Four different methods of VBQ determination with automatic segmentation are presented and compared with one-way ANOVA. These methods use different algorithms for CSF extraction from the segmentation of the spinal canal using T1- and T2-weighted image data and applying erosion to the vertebral ROI to avoid a sharp change in SI at the edge of the vertebral body.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleApplied Sciences
Numberingvol. 14, no. 22, 10343
e-ISSN2076-3417
DOIhttps://doi.org/10.3390/app142210343
URN:NBNurn:nbn:hr:184:946958
Publication2024-11-11
Document URLhttps://www.mdpi.com/2076-3417/14/22/10343
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-11-13 11:58:59