Automated Quantification of Cervical Spine Degeneration with Development of a Segmentation Framework Based on Probabilistic Anatomical Cognition.

Objective: Ossification of the posterior longitudinal ligament (OPLL) is a prevalent cervical spine degeneration disease leading to significant spinal cord dysfunctions. Due to morphological diversity and data scarcity, traditional OPLL assessment relies on manual measurements, which suffer from low consistency and high cost. To implement automated quantification of the OPLL, a cognition-inspired segmentation framework, named the probabilistic anatomical cognition (PAC) framework, is proposed to encode physicians' anatomical knowledge of the OPLL and mimic their hierarchical logic of inferring lesions.

Approach: The OPLL anatomical structure is firstly modeled by a multi-level probabilistic representation from the stochastic global shape of the spinal canal (SC) to the local feature distributions of the lesions. Based on the anatomical prior model, the OPLL segmentation is implemented by the deep-logic shape inference. The logic extracts high-confidence global feature observations of the SC, following with the inference to the local lesions by morphological correlations. The fusion of the anatomical prior and multi-level observations enhances both interpretability and generalization of lesion segmentation and reduces reliance on large datasets.

Main Results: Tested on a clinical dataset of 439 patients, the PAC framework improves DSC by 10% over the lightweight baseline and achieves high consistency with expert assessments on clinical lesion metrics.

Significance: A general automated segmentation pipeline and 3D metrics are provided for the first time by the framework to quantify the OPLL degeneration, which offers valuable insights to support surgical decision-making.