Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network
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 me...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10343 |
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| author | Kristian Stojšić Dina Miletić Rigo Slaven Jurković |
| author_facet | Kristian Stojšić Dina Miletić Rigo Slaven Jurković |
| author_sort | Kristian Stojšić |
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| description | 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. |
| format | Article |
| id | doaj-art-5e99eb2dfd2d40bcb3b2aa1b487090a8 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-5e99eb2dfd2d40bcb3b2aa1b487090a82024-11-26T17:48:23ZengMDPI AGApplied Sciences2076-34172024-11-0114221034310.3390/app142210343Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural NetworkKristian Stojšić0Dina Miletić Rigo1Slaven Jurković2Medical Physics and Radiation Protection Department, University Hospital Rijeka, 51000 Rijeka, CroatiaClinical Department of Diagnostic and Interventional Radiology, University Hospital Rijeka, 51000 Rijeka, CroatiaMedical Physics and Radiation Protection Department, University Hospital Rijeka, 51000 Rijeka, CroatiaVertebral 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.https://www.mdpi.com/2076-3417/14/22/10343MRICNNtransformerspine imagingVBQMRI segmentation |
| spellingShingle | Kristian Stojšić Dina Miletić Rigo 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 MRI CNN transformer spine imaging VBQ MRI segmentation |
| title | Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network |
| title_full | Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network |
| title_fullStr | Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network |
| title_full_unstemmed | Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network |
| title_short | Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network |
| title_sort | automated vertebral bone quality determination from t1 weighted lumbar spine mri data using a hybrid convolutional neural network transformer neural network |
| topic | MRI CNN transformer spine imaging VBQ MRI segmentation |
| url | https://www.mdpi.com/2076-3417/14/22/10343 |
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