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Spinal degeneration plagues many elders, office workers, and even the youngergenerations. Effective pharmic or surgical interventions can help relievedegenerative spine conditions. However, the traditional diagnosis procedure isoften too laborious. Clinical experts need to detect discs and vertebrae fromspinal magnetic resonance imaging (MRI) or computed tomography (CT) images as apreliminary step to perform pathological diagnosis or preoperative evaluation.Machine learning systems have been developed to aid this procedure generallyfollowing a two-stage methodology: first perform anatomical localization, thenpathological classification. Towards more efficient and accurate diagnosis, wepropose a one-stage detection framework termed SpineOne to simultaneouslylocalize and classify degenerative discs and vertebrae from MRI slices.SpineOne is built upon the following three key techniques: 1) a new design ofthe keypoint heatmap to facilitate simultaneous keypoint localization andclassification; 2) the use of attention modules to better differentiate therepresentations between discs and vertebrae; and 3) a novel gradient-guidedobjective association mechanism to associate multiple learning objectives atthe later training stage. Empirical results on the Spinal Disease IntelligentDiagnosis Tianchi Competition (SDID-TC) dataset of 550 exams demonstrate thatour approach surpasses existing methods by a large margin.

SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae