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About Deep Learning in DXA Image Segmentation
Many existing techniques to acquire dual-energy X-ray absorptiometry (DXA) images are unable to accurately distinguish between bone and soft tissue. For the most part, this failure stems from bone shape variability, noise and low contrast in DXA images, inconsistent X-ray beam penetration producing shadowing effects, and person-to-person variations. This work explores the feasibility of using state-of-the-art deep learning semantic segmentation models, fully convolutional networks (FCNs), SegNet
Detailed Information
| Author: | ['Dildar Hussain'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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