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且省时的方法，该网络可在坐标域或图像域中实现。然而，在关键点定位过程中，椎体可视化特征常导致异常拓扑结构，包括关键点边缘扭曲和弱连接结构，这些结构在单独坐标域或图像域中无法完全抑制。为克服这一限制，本文采用关键点-边缘模块和重参数化模块以跨域方式约束这些异常结构。关键点-边缘约束模块将关键点限制在椎体边缘，确保关键点坐标的分布模式与DCS量化一致。重参数化模块则结合坐标对图像域热图中的弱连接结构进行约束。此外，该跨域网络通过利用热图并融合坐标实现精确定位，从而提升空间泛化能力，避免了单一域中这两种属性之间的权衡。不同量化任务的综合结果表明，所提出的拓扑启发式跨域网络（TCN）在与其他竞争定位方法相比时具有优越性和泛化性。