Application of particle filter for vertebral body extraction: a simulation study

Lumbar vertebra motion analysis provides objective measurement of lumbar disorder. The automatic tracking algorithm has been applied to Digitalized Video Fluoroscopy (DVF) sequence. This paper proposes a new Auto-Tracking System (ATS) with a guide device and a motion analysis to automatically measure human lumbar motion. Digitalized Video Fluoroscopy (DVF) sequence was obtained during flexion-extension lumbar movement under guide device. An extraction of human vertebral body and its motion tracking were developed by particle filter. The results showed a good repeatability, reliability and robustness. In model test, the maximum fiducial error is 3.7% and the repeatability error is 1.2% in translation and the maximal repeatability error is 2.6% in rotation angle. In this simulation study, we employed a lumbar model to simulate the motion of lumber flexion- extension with the stepping translation of 1.3 mm and rotation angle of 1?. Results showed that the fiducial error was measured as 1.0%, while the repeatability error was 0.7%. The sequence can be detected even noise contamination as more as 0.5 of the density. The result demonstrates that the data from the auto-tracking algorithm shows a strong correlation with the actual measurement and that the Vertebral Auto-Tracking System (VATS) is highly repetitive. In the human lumbar spine evaluation, the study not only shows the reliability of Auto-Tracking Analysis System (ATAS), but also reveals that it is robust and variable in vivo. The VATS is evaluated by the model, the simulated sequence and the human subject. It could be concluded that the developed system could provide a reliable and robust system to detect spinal motion in future medical application.published_or_final_versio

Automatic lumbar motion analysis based on particle filtering

Spinal motion is produced by complex coordination of nerves and muscles and is constrained by vertebral structure. The observation and measurement of lumbar motion is of great value for clinical diagnosis and surgical plan of lumbar disorders. Digitalized Video Fluoroscopy (DVF) is the most suitable one to image the spine motion but it is quite time consuming. This paper proposes an automatic lumbar motion analysis system (ALMAS) with particle filtering technology. The automatically vertebral tracking for motion analysis was utilized with a friendly-interface, which provides a window for users to process the acquired DVF sequence and to analyze the tracking results. A set of simulation vertebra image were used to evaluate the performance and accuracy of this system. In simulated sequence, the maximal difference is 1.3 mm in translation and 1ͦ in rotation angle. The error is small in x- and y- translation (fiducial error: 2.4%, repeatability error: 0.5%) and in rotation angle (fiducial error: 1.0%, repeatability error: 0.7%). The ALMAS can still track the sequence contaminated by noise with the density ≤ 0.5. Besides, the results demonstrate that the data from the auto-tracking algorithm shows a strong correlation with the actual measurement and that the ALMAS is highly repetitive. Results from this study showed that ALMAS based on particle filtering are relatively robust and accurate for automatic lumbar motion analysis.published_or_final_versio

Auto-tracking system for human lumbar motion analysis

Previous lumbar motion analyses suggest the usefulness of quantitatively characterizing spine motion. However, the application of such measurements is still limited by the lack of user-friendly automatic spine motion analysis systems. This paper describes an automatic analysis system to measure lumbar spine disorders that consists of a spine motion guidance device, an X-ray imaging modality to acquire digitized video fluoroscopy (DVF) sequences and an automated tracking module with a graphical user interface (GUI). DVF sequences of the lumbar spine are recorded during flexion-extension under a guidance device. The automatic tracking software utilizing a particle filter locates the vertebra-of-interest in every frame of the sequence, and the tracking result is displayed on the GUI. Kinematic parameters are also extracted from the tracking results for motion analysis. We observed that, in a bone model test, the maximum fiducial error was 3.7%, and the maximum repeatability error in translation and rotation was 1.2% and 2.6%, respectively. In our simulated DVF sequence study, the automatic tracking was not successful when the noise intensity was greater than 0.50. In a noisy situation, the maximal difference was 1.3 mm in translation and 1° in the rotation angle. The errors were calculated in translation (fiducial error: 2.4%, repeatability error: 0.5%) and in the rotation angle (fiducial error: 1.0%, repeatability error: 0.7%). However, the automatic tracking software could successfully track simulated sequences contaminated by noise at a density ≤ 0.5 with very high accuracy, providing good reliability and robustness. A clinical trial with 10 healthy subjects and 2 lumbar spondylolisthesis patients were enrolled in this study. The measurement with auto-tacking of DVF provided some information not seen in the conventional X-ray. The results proposed the potential use of the proposed system for clinical applications. © 2011 - IOS Press and the authors. All rights reserved.postprin

Automatic demarcation for videofluoroscopy swallowing study

Videofluoroscopy tests are designed to analyze the swallowing response of the patient. Lot of patients die due to swallowing disorders, and physicians want to analyze this procedure to detect this swallowing problems. The principal problem is that the time spent to analyze a video is so long, and has to be done manually, and this derives to a high cost of time and money to the health system. Hyoid Marker is an application that provides an automatic demarcation for videofluoroscopy studies, saving to the physician the time to mark important objects in the video, and allowing him to dedicate his time analyzing only the medical aspects of the video

An automated optimization pipeline for clinical-grade computer-assisted planning of high tibial osteotomies under consideration of weight-bearing

3D preoperative planning for high tibial osteotomies (HTO) has increasingly replaced 2D planning but is complex, time-consuming and therefore expensive. Several interdependent clinical objectives and constraints have to be considered, which often requires multiple rounds of revisions between surgeons and biomedical engineers. We therefore developed an automated preoperative planning pipeline, which takes imaging data as an input to generate a ready-to-use, patient-specific planning solution. Deep-learning based segmentation and landmark localization was used to enable the fully automated 3D lower limb deformity assessment. A 2D-3D registration algorithm allowed the transformation of the 3D bone models into the weight-bearing state. Finally, an optimization framework was implemented to generate ready-to use preoperative plannings in a fully automated fashion, using a genetic algorithm to solve the multi-objective optimization (MOO) problem based on several clinical requirements and constraints. The entire pipeline was evaluated on a large clinical dataset of 53 patient cases who previously underwent a medial opening-wedge HTO. The pipeline was used to automatically generate preoperative solutions for these patients. Five experts blindly compared the automatically generated solutions to the previously generated manual plannings. The overall mean rating for the algorithm-generated solutions was better than for the manual solutions. In 90% of all comparisons, they were considered to be equally good or better than the manual solution. The combined use of deep learning approaches, registration methods and MOO can reliably produce ready-to-use preoperative solutions that significantly reduce human workload and related health costs

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields