Foetus-specific neuromusculoskeletal modelling with MRI-driven vaginal delivery kinematics during the second stage of labor.

Childbirth simulations lack realism due to an oversimplification of the foetal model, particularly as most models do not allow joint motion. Foetus-specific neuromusculoskeletal (NMS) model with a detailed articulated skeleton is still not available in the literature. The present work aims at proposing the first-ever foetus-specific NMS model and then simulating the foetal descent during a vaginal delivery by using medical resonance imaging (MRI) childbirth data.

A user-friendly deep learning application for accurate lung cancer diagnosis.

Background: Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faster high-quality diagnoses, but the accuracy and precision of 3-D image processing from 2-D data may be limited by factors such as superposition of organs, distortion and magnification, and detection of new pathologies. The purpose of this research is to use radiomics and deep learning to develop a tool for lung cancer diagnosis.

Novel Baseline Facial Muscle Database Using Statistical Shape Modeling and In Silico Trials toward Decision Support for Facial Rehabilitation.

: Facial palsy is a complex pathophysiological condition affecting the personal and professional lives of the involved patients. Sudden muscle weakness or paralysis needs to be rehabilitated to recover a symmetric and expressive face. Computer-aided decision support systems for facial rehabilitation have been developed.

Fast 3D Face Reconstruction from a Single Image Using Different Deep Learning Approaches for Facial Palsy Patients.

The 3D reconstruction of an accurate face model is essential for delivering reliable feedback for clinical decision support. Medical imaging and specific depth sensors are accurate but not suitable for an easy-to-use and portable tool. The recent development of deep learning (DL) models opens new challenges for 3D shape reconstruction from a single image.

Enhanced head-skull shape learning using statistical modeling and topological features.

Skull prediction from the head is a challenging issue toward a cost-effective therapeutic solution for facial disorders. This issue was initially studied in our previous work using full head-to-skull relationship learning. However, the head-skull thickness topology is locally shaped, especially in the face region.

Kinect-driven Patient-specific Head, Skull, and Muscle Network Modelling for Facial Palsy Patients.

Background And Objective: Facial palsy negatively affects both professional and personal life qualities of involved patients. Classical facial rehabilitation strategies can recover facial mimics into their normal and symmetrical movements and appearances. However, there is a lack of objective, quantitative, and in-vivo facial texture and muscle activation bio-feedbacks for personalizing rehabilitation programs and diagnosing recovering progresses.

A statistical shape modeling approach for predicting subject-specific human skull from head surface.

Human skull is an important body structure for jaw movement and facial mimic simulations. Surface head can be reconstructed using 3D scanners in a straightforward way. However, internal skull is challenging to be generated when only external information is available.

A Systematic Review of Real-Time Medical Simulations with Soft-Tissue Deformation: Computational Approaches, Interaction Devices, System Architectures, and Clinical Validations.

Simulating deformations of soft tissues is a complex engineering task, and it is even more difficult when facing the constraint between computation speed and system accuracy. However, literature lacks of a holistic review of all necessary aspects (computational approaches, interaction devices, system architectures, and clinical validations) for developing an effective system of soft-tissue simulations. This paper summarizes and analyses recent achievements of resolving these issues to estimate general trends and weakness for future developments.

Real-time computer vision system for tracking simultaneously subject-specific rigid head and non-rigid facial mimic movements using a contactless sensor and system of systems approach.

Background And Objective: Head and facial mimic animations play important roles in various fields such as human-machine interactions, internet communications, multimedia applications, and facial mimic analysis. Numerous studies have been trying to simulate these animations. However, they hardly achieved all requirements of full rigid head and non-rigid facial mimic animations in a subject-specific manner with real-time framerates.