Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network-Transformer Neural Network

被引:0
|
作者
Stojsic, Kristian [1 ]
Miletic Rigo, Dina [2 ,3 ]
Jurkovic, Slaven [1 ,4 ]
机构
[1] Univ Hosp Rijeka, Med Phys & Radiat Protect Dept, Rijeka 51000, Croatia
[2] Univ Hosp Rijeka, Clin Dept Diagnost & Intervent Radiol, Rijeka 51000, Croatia
[3] Univ Rijeka, Fac Med, Dept Radiol, Rijeka 51000, Croatia
[4] Univ Rijeka, Fac Med, Dept Med Phys & Biophys, Rijeka 51000, Croatia
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 22期
关键词
MRI; CNN; transformer; spine imaging; VBQ; MRI segmentation; OSTEOPOROSIS; PREVENTION;
D O I
10.3390/app142210343
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
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页数:15
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