An Automatic Grading System for Neonatal Endotracheal Intubation with Multi-Task Convolutional Neural Network.

Unlabelled: Neonatal endotracheal intubation (ETI) is an intricate medical procedure that poses considerable challenges, demanding comprehensive training to effectively address potential complications in clinical practice. However, due to limited access to clinical opportunities, ETI training relies heavily on physical manikins to develop a certain level of competence before clinical exposure. Nonetheless, traditional training methods prove ineffective due to scarcity of expert instructors and the absence of internal situational awareness within the manikins, preventing thorough performance assessment for both trainees and instructors.

3D Body shape for regional and appendicular body composition estimation.

Body composition is correlated to bone mineral density, muscle strength, and physical performance. This is important for diagnosing conditions like sarcopenia, which is defined as the age-associated decrease in muscle mass leading to decreased mobile function, increased frailty, and imbalance. Existing methods for body composition measurement either suffer from inaccurate results or require expensive equipment such as Dual-energy x-ray absorptiometry (DXA).

Liver Fat Assessment with Body Shape.

Hepatic steatosis has become a serious health concern among the general population, but especially for those who are obese. Liver fat can increase the risk of cirrhosis and even liver cancer. Current standard methods to assess hepatic steatosis, such as liver biopsy and CT/MR imaging techniques, are expensive and/or may have associated risks to health.

Automated Assessment System for Neonatal Endotracheal Intubation Using Dilated Convolutional Neural Network.

Neonatal endotracheal intubation (ETI) is an important, complex resuscitation skill, which requires a significant amount of practice to master. Current ETI practice is conducted on the physical manikin and relies on the expert instructors' assessment. Since the training opportunities are limited by the availability of expert instructors, an automatic assessment model is highly desirable.

Robust Template-Based Non-Rigid Motion Tracking Using Local Coordinate Regularization.

In this paper, we propose our template-based non-rigid registration algorithm to address the misalignments in the frame-to-frame motion tracking with single or multiple commodity depth cameras. We analyze the deformation in the local coordinates of neighboring nodes and use this differential representation to formulate the regularization term for the deformation field in our non-rigid registration. The local coordinate regularizations vary for each pair of neighboring nodes based on the tracking status of the surface regions.

A Novel Hybrid Model for Visceral Adipose Tissue Prediction using Shape Descriptors.

Obesity is gaining increasing attention in modern society since it is associated with various health issues. The visceral adipose tissue (VAT) deposits around the abdominal organs and is considered an extremely important indicator of health risk. VAT can be assessed through magnetic resonance imaging (MRI) or computed tomography (CT) accurately, but the cost is prohibitive.

Accurate nonrigid 3D human body surface reconstruction using commodity depth sensors.

In the last decade, 3D modeling techniques enjoyed a booming development in both hardware and software. High-end hardware generates high fidelity results, but the cost is prohibitive, whereas consumer-level devices generate plausible results for entertainment purposes but are not appropriate for medical uses. We present a cost-effective and easy-to-use 3D body reconstruction system using consumer-grade depth sensors, which provides reconstructed body shapes with a high degree of accuracy and reliability appropriate for medical applications.

Shape-based Three-dimensional Body Composition Extrapolation Using Multimodality Registration.

The ubiquity of commodity-level optical scan devices and reconstruction technologies has enabled the general public to monitor their body shape related health status anywhere, anytime, without assistance from professionals. Commercial optical body scan systems extract anthropometries from the virtual body shapes, from which body compositions are estimated. However, in most cases, these estimations are limited to the quantity of fat in the whole body instead of a fine-granularity voxel-level fat distribution estimation.

ReTouchImg: Fusioning from-local-to-global context detection and graph data structures for fully-automatic specular reflection removal for endoscopic images.

Minimally invasive surgical and diagnostic systems are commonly used in clinical practices. However, the accuracy and robustness of these systems depend heavily on computer based processes such as tracking, detecting or segmenting clinically meaningful regions of interest, which are significantly affected by the inherent specular reflections that appear on the organs' surfaces. Restoration of the acquired data for clinical purposes still presents challenges because of the high texture and color variations across the image.

3D Shape-Based Body Composition Inference Model Using a Bayesian Network.

Body composition can be assessed in many different ways. High-end medical equipment, such as Dual-energy X-ray Absorptiometry (DXA), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) offers high-fidelity pixel/voxel-level assessment, but is prohibitive in cost. In the case of DXA and CT, the approach exposes users to ionizing radiation.

3D Shape-based Body Composition Prediction Model Using Machine Learning.

A booming development of 3D body scan and modeling technologies has facilitated large-scale anthropometric data collections for biomedical research and applications. However, usages of the digitalized human body shape data are relatively limited due to a lack of corresponding medical data to establish correlations between body shapes and underlying health information, such as the Body Fat Percentage (BFP). We present a novel prediction model to estimate the BFP by analyzing 3D body shapes.

Evaluation of performance, acceptance, and compliance of an auto-injector in healthy and rheumatoid arthritic subjects measured by a motion capture system.

Purpose: The study aimed to develop a motion capture system that can track, visualize, and analyze the entire performance of self-injection with the auto-injector.

Methods: Each of nine healthy subjects and 29 rheumatoid arthritic (RA) patients with different degrees of hand disability performed two simulated injections into an injection pad while six degrees of freedom (DOF) motions of the auto-injector and the injection pad were captured. We quantitatively measured the performance of the injection by calculating needle displacement from the motion trajectories.

Adaptive segmentation and mask-specific Sobolev inpainting of specular highlights for endoscopic images.

Minimally invasive surgical and diagnostic systems rely on endoscopic images of internal organs to assist medical tasks. Specular highlights are common on those images due to the strong reflectivity of the mucus layer on the organs and the relatively high intensity of the light source. This is a significant source of error that can affect the systems' performance.

Towards Retrieving Force Feedback in Robotic-Assisted Surgery: A Supervised Neuro-Recurrent-Vision Approach.

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure.

Automatic and robust single-camera specular highlight removal in cardiac images.

In computer-assisted beating heart surgeries, accurate tracking of the heart's motion is of huge importance and there is a continuous need to eliminate any source of error that might disturb the tracking process. One source of error is the specular reflection that appears on the glossy surface of the heart. In this paper, we propose a robust solution for the detection and removal of specular highlights.

Computer-based Planning System for Mandibular Reconstruction.

Mandibular reconstruction is typically performed for traumatic or postsurgical conditions, and may involve the use of autologous osteocutaneous fibula free flaps for large defects. Recreating the native contour of the mandible during reconstructive surgery is challenging. Existed pre-operative planning software has limitations.

An immersed-boundary method for flow-structure interaction in biological systems with application to phonation.

A new numerical approach for modeling a class of flow-structure interaction problems typically encountered in biological systems is presented. In this approach, a previously developed, sharp-interface, immersed-boundary method for incompressible flows is used to model the fluid flow and a new, sharp-interface Cartesian grid, immersed boundary method is devised to solve the equations of linear viscoelasticity that governs the solid. The two solvers are coupled to model flow-structure interaction.

Image guided medialization laryngoplasty.

Techniques that originate in computer graphics and computer vision have found prominent applications in the medical domain. In this paper, we have seamlessly developed techniques from computer graphics and computer vision together with domain knowledge from medicine to develop an image guided surgical system for medialization laryngoplasty. The technical focus of this paper is to register the preoperative radiological data to the intraoperative anatomical structure of the patient.

Cryotherapy simulator for localized prostate cancer.

Cryotherapy is a treatment modality that uses a technique to selectively freeze tissue and thereby cause controlled tissue destruction. The procedure involves placement of multiple small diameter probes through the perineum into the prostate tissue at selected spatial intervals. Transrectal ultrasound is used to properly position the cylindrical probes before activation of the liquid Argon cooling element, which lowers the tissue temperature below -40 degrees Centigrade.