Automatic and precise segmentation of vertebrae from CT images is crucial for various clinical applications. However, due to a lack of explicit and strict 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, is affected by the pathology and mental implants. Thus, incorrect detections cause biased patches before segmentation, then lead to inconsistent labeling and segmentation. In our work, motivated by the perspective of instance segmentation, we try to label individual and complete binary masks to address this limitation. Specifically, a contour-based network is proposed based on Structural Low-Rank Descriptors for shape consistency, termed SLoRD. These contour descriptors are acquired in a data-driven manner in advance. For a more precise representation of contour descriptors, we adopt the spherical coordinate system and devise the spherical centroid. Besides, the contour loss is designed to impose explicit consistency constraints, facilitating regressed contour points close to vertebral boundaries. Quantitative and qualitative evaluations on VerSe 2019 demonstrate the superior performance of our framework over other single-stage and multi-stage state-of-the-art (SOTA) methods.

翻译：从CT图像中自动且精确地分割椎骨对于多种临床应用至关重要。然而，由于缺乏显式和严格的约束，现有方法，特别是单阶段方法，仍然面临椎骨内分割不一致性的挑战，即单个椎骨内部出现多个标签预测。对于多阶段方法，作为第一步的椎骨检测会受到病理和金属植入物的影响。因此，错误的检测会导致分割前的图像块存在偏差，进而导致不一致的标记和分割。在我们的工作中，受实例分割视角的启发，我们尝试标记独立且完整的二值掩码以解决这一局限。具体而言，我们提出了一种基于结构低秩描述符以实现形状一致性的轮廓网络，称为SLoRD。这些轮廓描述符以数据驱动的方式预先获取。为了更精确地表示轮廓描述符，我们采用球坐标系并设计了球面质心。此外，我们设计了轮廓损失以施加显式的一致性约束，促使回归的轮廓点接近椎骨边界。在VerSe 2019数据集上的定量和定性评估表明，我们的框架优于其他单阶段和多阶段的先进方法。