Search

Although the task-specific robot treadmill therapies can improve gait performance of children with cerebral palsy, not all the institutions have the possibility to access to the sensor robot information to obtain quantitative measures related actual progress of the children. The aim of this study is to propose a computer vision system to investigate the effort of children with cerebral palsy during robot treadmill therapy. Ten participants diagnosed with cerebral palsy underwent single robot therapy sessions consisting of a motivated period (active: kicking a ball), followed by an unmotivated period (passive: simple walking). The outcome measure was the ankle trajectory recorded by a video camera. Indexes based on the ankle trajectory between active and passive therapy were computed: (a) the gait analysis based on the difference on a sample-by-sample manner, and b) the step cycles with dynamic time warping (DTW). Inference statistical analysis shows that the robot trajectories for passive and active therapies with the gait analysis presents statistical differences (p- value < 0. 0 1)). Likewise, DTW showed significant differences in robot trajectories related to passive and active therapy (p- value < 0. 0 1)). We found that the statistical differences are independent of the severity of central gait impairment. This suggests that information about patient involvement could be given to the therapist and could be used to assess patient participation. The method showed the potential to be used as a tool for the assessment the improvement progress. [ABSTRACT FROM AUTHOR]

Copyright of Revista Mexicana de Ingeniería Biomédica is the property of Sociedad Mexicana de Ingenieria Biomedica, A.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A two-step method for obtaining a volumetric estimation of COVID-19 related lesion from CT images is proposed. The first step consists in applying a U-NET convolutional neural network to provide a segmentation of the lung-parenchyma. This architecture is trained and validated using the Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines (PleThora) dataset, which is publicly available. The second step consists in obtaining the volumetric lesion estimation using an automatic algorithm based on a probabilistic active contour (PACO) region delimitation approach. Our pipeline successfully segmented COVID-19 related lesions in CT images, with exception of some mislabeled regions including lung airways and vasculature. Our workflow was applied to images in a cohort of 50 patients.

In this paper a method based on mesh surfaces approximations for the 3D analysis of anatomical structures in Radiotherapy (RT) is presented. Parotid glands meshes constructed from Megavoltage CT (MVCT) images were studied in terms of volume, distance between center of mass (distCOM) of the right and left parotids, dice similarity coefficient (DICE), maximum distance between meshes (DMax) and the average symmetric distance (ASD). A comparison with the standard binary images approach was performed. While absence of significant differences in terms of volume, DistCOM and DICE indices suggests that both approaches are comparable, the fact that the ASD showed significant difference (p=0.002) and the DMax was almost significant (p=0.053) suggests that the mesh approach should be adopted to provide accurate comparison between 3D anatomical structures of interest in RT.

A novel method for approximate string matching with applications to bioinformatics is presented in this paper. Unlike most methods in the literature, the proposed method does not depend on the computation of the edit distance between two sequences, but uses instead a similarity index obtained by applying the phase correlation method. The resulting algorithm provides a finer control over the false positive rate, allowing users to pick out relevant matchings in less time, and can be applied for both offline and online processing.