Developmental Canal Stenosis (DCS) quantification is crucial in cervical spondylosis screening. Compared with quantifying DCS manually, a more efficient and time-saving manner is provided by deep keypoint localization networks, which can be implemented in either the coordinate or the image domain. However, the vertebral visualization features often lead to abnormal topological structures during keypoint localization, including keypoint distortion with edges and weakly connected structures, which cannot be fully suppressed in either the coordinate or image domain alone. To overcome this limitation, a keypoint-edge and a reparameterization modules are utilized to restrict these abnormal structures in a cross-domain manner. The keypoint-edge constraint module restricts the keypoints on the edges of vertebrae, which ensures that the distribution pattern of keypoint coordinates is consistent with those for DCS quantification. And the reparameterization module constrains the weakly connected structures in image-domain heatmaps with coordinates combined. Moreover, the cross-domain network improves spatial generalization by utilizing heatmaps and incorporating coordinates for accurate localization, which avoids the trade-off between these two properties in an individual domain. Comprehensive results of distinct quantification tasks show the superiority and generability of the proposed Topology-inspired Cross-domain Network (TCN) compared with other competing localization methods.

翻译：发育性颈椎管狭窄（DCS）的量化在颈椎病筛查中至关重要。与手动量化DCS相比，深度关键点定位网络提供了一种更高效、更省时的方法，可在坐标域或图像域中实现。然而，椎骨可视化特征常导致关键点定位过程中出现异常拓扑结构，包括具有边缘的关键点扭曲和弱连接结构，这些异常在单独坐标域或图像域中无法完全抑制。为克服这一限制，采用关键点边缘约束模块和重参数化模块以跨领域方式限制这些异常结构。关键点边缘约束模块将关键点限制在椎骨边缘，确保关键点坐标分布模式与DCS量化结果一致。重参数化模块则将弱连接结构约束于结合坐标的图像域热图中。此外，跨领域网络通过利用热图并融合坐标进行精确定位提升空间泛化能力，避免了单一领域中两种特性之间的权衡。不同量化任务的综合结果证明了所提出的拓扑启发式跨领域网络（TCN）相较于其他竞争性定位方法的优越性和泛化能力。