Three-Dimensional Anatomical Analysis of Muscle–Skeletal Districts

Abstract

This work addresses the patient-specific characterisation of the morphology and pathologies of muscle–skeletal districts (e.g., wrist, spine) to support diagnostic activities and follow-up exams through the integration of morphological and tissue information. We propose different methods for the integration of morphological information, retrieved from the geometrical analysis of 3D surface models, with tissue information extracted from volume images. For the qualitative and quantitative validation, we discuss the localisation of bone erosion sites on the wrists to monitor rheumatic diseases and the characterisation of the three functional regions of the spinal vertebrae to study the presence of osteoporotic fractures. The proposed approach supports the quantitative and visual evaluation of possible damages, surgery planning, and early diagnosis or follow-up studies. Finally, our analysis is general enough to be applied to different districts.

Learning-based low-rank denoising

“…We discuss two variants of this problem: the first one is the bound-constraint version, which is typical of image processing as the signal is defined on a regular grid [CP21]; the second one is the non-linear geometric constraint version, which is typical of signals on graphs/meshes [PPS22]. The two variants share the same objective function; however, the non-linear geometric constraints affect the selection and analysis of the minimisation method.…”