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Segmentation of Intervertebral Disc based on Semi-supervised Conditional Generative Adversarial Network

Intervertebral disc herniation has now become a highly prevalent disease among white collars, and commonly, it's preoperative planning as well as rehabilitation protocols are reliant on physicians’ manual reading of spinal magnetic resonance images to accomplish the desired outcomes. With the advancement of computer technology and artificial intelligence in recent years, a growing number of diagnostic algorithms have been developed for the analysis of spinal lesion images and automatical generation of high-precision lesion segmentation areas, which can provide great aid to imaging physicians in clinical diagnosis and preoperative planning. Even so, poor accuracy with few labels in under-annotated background images poses a great challenge for semantic segmentation of the spine. Because of this, we designed a semi-supervised semantic segmentation network for spine images based on conditional adversarial neural networks combined with U-net and Tversky loss to address the above issue. The accuracy reached 0.898 for the optimal network performance. Finally, we verified the feasibility and superiority of the proposed algorithm in the semi-supervised semantic segmentation task of intervertebral disc images.