937 research outputs found
A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images
Purpose: Proximal femur image analyses based on quantitative computed
tomography (QCT) provide a method to quantify the bone density and evaluate
osteoporosis and risk of fracture. We aim to develop a deep-learning-based
method for automatic proximal femur segmentation. Methods and Materials: We
developed a 3D image segmentation method based on V-Net, an end-to-end fully
convolutional neural network (CNN), to extract the proximal femur QCT images
automatically. The proposed V-net methodology adopts a compound loss function,
which includes a Dice loss and a L2 regularizer. We performed experiments to
evaluate the effectiveness of the proposed segmentation method. In the
experiments, a QCT dataset which included 397 QCT subjects was used. For the
QCT image of each subject, the ground truth for the proximal femur was
delineated by a well-trained scientist. During the experiments for the entire
cohort then for male and female subjects separately, 90% of the subjects were
used in 10-fold cross-validation for training and internal validation, and to
select the optimal parameters of the proposed models; the rest of the subjects
were used to evaluate the performance of models. Results: Visual comparison
demonstrated high agreement between the model prediction and ground truth
contours of the proximal femur portion of the QCT images. In the entire cohort,
the proposed model achieved a Dice score of 0.9815, a sensitivity of 0.9852 and
a specificity of 0.9992. In addition, an R2 score of 0.9956 (p<0.001) was
obtained when comparing the volumes measured by our model prediction with the
ground truth. Conclusion: This method shows a great promise for clinical
application to QCT and QCT-based finite element analysis of the proximal femur
for evaluating osteoporosis and hip fracture risk
ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation
We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance
Biomechanical importance of proximal human femur morphology and mechanics in orthopaedic purposes
Bone morphology is essential in orthopedic surgery to perform precise preoperative planning and surgery as well as to appropriately design optimal medical implants. In this study we provided a database of surgically important morphological parameters of proximal human femur for orthopedic and biomedical research purposes (study 1), indicated accuracy of the 3D reconstructed images in comparison with the optical 3D scan of real human femur (study 2), and reported the accuracy and reliability of the developed image-based finite element model in comparison with the experimental results (study 3)
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