In this dissertation, the performance of the in-house developed deep learning algorithm for vertebral body detection and segmentation was investigated in 160 CT, and DXA images. To evaluate the segmentation precision of the convolutional neural network, a comparison was made with manually corrected segmentation masks in 43 cases. By collecting the Dice score per vertebral body, patient-, and image-specific influencing factors for the error-proneness of the algorithm could be analyzed and pointed out.
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In this dissertation, the performance of the in-house developed deep learning algorithm for vertebral body detection and segmentation was investigated in 160 CT, and DXA images. To evaluate the segmentation precision of the convolutional neural network, a comparison was made with manually corrected segmentation masks in 43 cases. By collecting the Dice score per vertebral body, patient-, and image-specific influencing factors for the error-proneness of the algorithm could be analyzed and pointed...
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