arXiv subjects

Martina Paccini

Publications and source records attributed to Martina Paccini.

5 recordsLinked to original sources

arXiv2025

Framework of a multiscale data-driven DT of the musculoskeletal system

Musculoskeletal disorders (MSDs) are a leading cause of disability worldwide, requiring advanced diagnostic and therapeutic tools for personalised assessment and treatment. Effective management of MSDs involves the interaction of heterogeneous data sources, making the Digital Twin (DT) paradigm a valuable option. This paper introduces the Musculoskeletal Digital Twin (MS-DT), a novel framework that integrates multiscale biomechanical data with computational modelling to create a detailed, patient-specific representation of the musculoskeletal system. By combining motion capture, ultrasound imaging, electromyography, and medical imaging, the MS-DT enables the analysis of spinal kinematics, posture, and muscle function. An interactive visualisation platform provides clinicians and researchers with an intuitive interface for exploring biomechanical parameters and tracking patient-specific changes. Results demonstrate the effectiveness of MS-DT in extracting precise kinematic and dynamic tissue features, offering a comprehensive tool for monitoring spine biomechanics and rehabilitation. This framework provides high-fidelity modelling and real-time visualization to improve patient-specific diagnosis and intervention planning.

eess.IV↗

arXiv2025

3D Skin Segmentation Methods in Medical Imaging: A Comparison

Automatic segmentation of anatomical structures is critical in medical image analysis, aiding diagnostics and treatment planning. Skin segmentation plays a key role in registering and visualising multimodal imaging data. 3D skin segmentation enables applications in personalised medicine, surgical planning, and remote monitoring, offering realistic patient models for treatment simulation, procedural visualisation, and continuous condition tracking. This paper analyses and compares algorithmic and AI-driven skin segmentation approaches, emphasising key factors to consider when selecting a strategy based on data availability and application requirements. We evaluate an iterative region-growing algorithm and the TotalSegmentator, a deep learning-based approach, across different imaging modalities and anatomical regions. Our tests show that AI segmentation excels in automation but struggles with MRI due to its CT-based training, while the graphics-based method performs better for MRIs but introduces more noise. AI-driven segmentation also automates patient bed removal in CT, whereas the graphics-based method requires manual intervention.

eess.IV↗

arXiv2024

US \& MRI Image Fusion Based on Markerless Skin Registration

This paper presents an innovative automatic fusion imaging system that combines 3D CT/MR images with real-time ultrasound (US) acquisition. The system eliminates the need for external physical markers and complex training, making image fusion feasible for physicians with different experience levels. The integrated system involves a portable 3D camera for patient-specific surface acquisition, an electromagnetic tracking system, and US components. The fusion algorithm comprises two main parts: skin segmentation and rigid co-registration, both integrated into the US machine. The co-registration software aligns the surface extracted from CT/MR images with patient-specific coordinates, facilitating rapid and effective fusion. Experimental testing in different settings validates the system's accuracy, computational efficiency, noise robustness, and operator independence. The co-registration error remains under the acceptable range of~$1$ cm.

cs.CV↗

arXiv2023

3D Patient-specific Modelling and Characterisation of Muscle-Skeletal Districts

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 will 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.

cs.CV↗

arXiv2022

3D Anatomical Representations and Analysis: an Application to the Spine

This work proposes a framework for the patient-specific characterization of the spine, which integrates information on the tissues with geometric information on the spine morphology. Key elements are the extraction of 3D patient-specific models of each vertebra and the intervertebral space from 3D CT images, the segmentation of each vertebra in its three functional regions, and the analysis of the tissue condition in the functional regions based on geometrical parameters. The localization of anomalies obtained in the results and the proposed visualization support the applicability of our tool for quantitative and visual evaluation of possible damages, for surgery planning, and early diagnosis or follow-up studies. Finally, we discuss the main properties of the proposed framework in terms of characterisation of the morphology and pathology of the spine on benchmarks of the spine district.

cs.GR↗