DCES-PA: Deformation-controllable elastic shape model for 3D bone proliferation analysis using hand HR-pQCT images

• Published in: Computers in biology and medicine Vol. 175; p. 108533
• Main Authors: Zhang, Xuechen, Cheng, Isaac, Jin, Yingzhao, Shi, Jiandong, Li, Chenrui, Xue, Jing-Hao, Tam, Lai-Shan, Yu, Weichuan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2024; Elsevier Limited
• Subjects: Algorithms; Annotations; Applied differential geometry; Arthritis; Biology; Bone proliferation analysis; Bones; Computed tomography; Controllability; Decomposition; Deep learning; Deformation; Elastic deformation; Elastic Riemannian metric; Fields (mathematics); Fines & penalties; Inflammatory rheumatic disease; Medicine; Methods; Morphology; Physicians; Rheumatic diseases; Shearing; Smoothness; Statistical shape analysis; Tomography; Statistical shape analysis; Elastic Riemannian metric; Applied differential geometry; Inflammatory rheumatic disease; Bone proliferation analysis
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108533

Bone proliferation is an important pathological feature of inflammatory rheumatic diseases. Although recent advance in high-resolution peripheral quantitative computed tomography (HR-pQCT) enables physicians to study microarchitectures, physicians’ annotation of proliferation suffers from slice inconsistency and subjective variations. Also, there are only few effective automatic or semi-automatic tools for proliferation detection. In this study, by integrating pathological knowledge of proliferation formation with the advancement of statistical shape analysis theory, we present an unsupervised method, named Deformation-Controllable Elastic Shape model, for 3D bone Proliferation Analysis (DCES-PA). Unlike previous shape analysis methods that directly regularize the smoothness of the displacement field, DCES-PA regularizes the first and second-order derivative of the displacement field and decomposes these vector fields according to different deformations. For the first-order elastic metric, DCES-PA orthogonally decomposes the first-order derivative of the displacement field by shearing, scaling and bending deformation, and then penalize deformations triggering proliferation formation. For the second-order elastic metric, DCES-PA encodes both intrinsic and extrinsic surface curvatures into the second-order derivative of the displacement field to control the generation of high-curvature regions. By integrating the elastic shape metric with the varifold distances, DCES-PA achieves correspondence-free shape analysis. Extensive experiments on both simulated and real clinical datasets demonstrate that DCES-PA not only shows an improved accuracy than other state-of-the-art shape-based methods applied to proliferation analysis but also produces highly sensitive proliferation annotations to assist physicians in proliferation analysis. •A deformation-controllable elastic shape model for 3D joint bone proliferation analysis.•An extrinsic second-order metric complementing the widely-used Laplacian-Beltrami one.•The new method is unsupervised, and its performance is better than existing shape-based analysis methods in both simulated and real clinical datasets.