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Effectiveness of Pseudo 3D Feature Learning for Spinal Segmentation by CNN with U-Net Architecture
2019
Journal of Image and Graphics
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In the medical field, automatic extraction of spinal region from CT images has been desired. Among various methods for image segmentation, one of the convolutional neural network models called U-Net [1] has been shown to attain good performance with small data set size. Previous study by Kamata et al. [2] applied U-Net for spine segmentation task and achieved 82.7% accuracy for unlearned CT images. However, the method had difficulty in the precision of the 3D shape. This study attempted
doi:10.18178/joig.7.3.107-111
fatcat:3nbj2tvnyrh6vjahcypuynemia