@article{discovery10192642, month = {June}, publisher = {Elsevier}, note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.}, volume = {175}, title = {DCES-PA: Deformation-controllable elastic shape model for 3D bone proliferation analysis using hand HR-pQCT images}, journal = {Computers in Biology and Medicine}, year = {2024}, keywords = {Elastic Riemannian metric; Statistical shape analysis; Applied differential geometry; Inflammatory rheumatic disease; Bone proliferation analysis}, issn = {0010-4825}, author = {Zhang, Xuechen and Cheng, Isaac and Jin, Yingzhao and Shi, Jiandong and Li, Chenrui and Xue, Jing-Hao and Tam, Lai-Shan and Yu, Weichuan}, url = {http://dx.doi.org/10.1016/j.compbiomed.2024.108533}, abstract = {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.} }