%0 Journal Article %@ 0010-4825 %A Zhang, Xuechen %A Cheng, Isaac %A Jin, Yingzhao %A Shi, Jiandong %A Li, Chenrui %A Xue, Jing-Hao %A Tam, Lai-Shan %A Yu, Weichuan %D 2024 %F discovery:10192642 %I Elsevier %J Computers in Biology and Medicine %K Elastic Riemannian metric; Statistical shape analysis; Applied differential geometry; Inflammatory rheumatic disease; Bone proliferation analysis %T DCES-PA: Deformation-controllable elastic shape model for 3D bone proliferation analysis using hand HR-pQCT images %U https://discovery.ucl.ac.uk/id/eprint/10192642/ %V 175 %X 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. %Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.