SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation

Xin You, Yixin Lou, Minghui Zhang, Chuyan Zhang, Jie Yang, Yun Gu

Abstract: Automatic and precise multi-class vertebrae segmentation from CT images is crucial for various clinical applications. However, due to a lack of explicit consistency constraints, existing methods especially for single-stage methods, still suffer from the challenge of intra-vertebrae segmentation inconsistency, which refers to multiple label predictions inside a singular vertebra. For multi-stage methods, vertebrae detection serving as the first step, tends to be affected by the pathology and metal implants. Thus, imprecise detections cause biased patches before segmentation, which then leads to inaccurate contour delineation and inconsistent segmentation. In our work, we intend to label individual and complete binary masks to address that challenge. Specifically, a contour generation network is proposed based on Structural Low-Rank Descriptors for shape consistency, termed SLoRD. For a structural representation of vertebral contours, we adopt the spherical coordinate system and devise the spherical centroid to calculate contour descriptors. Due to vertebrae's similar appearances, basic contour descriptors can be acquired to restore original contours. Therefore, SLoRD leverages these contour priors and explicit shape constraints to facilitate regressed contour points close to vertebral surfaces. Quantitative and qualitative evaluations on VerSe 2019 and 2020 demonstrate the superior performance of our framework over other single-stage and multi-stage state-of-the-art (SOTA) methods. Further, SLoRD is a plug-and-play framework to refine the segmentation inconsistency existing in coarse predictions from other approaches.