Learning Environments and Evidence-Based Practices in Bioengineering and Biomedical Engineering.

This paper provides a synopsis of discussions related to the Learning Environments track of the Fourth BME Education Summit held at Case Western Reserve University in Cleveland, Ohio in May 2019. This summit was organized by the Council of Chairs of Bioengineering and Biomedical Engineering, and participants included over 300 faculty members from 100+ accredited undergraduate programs. The Learning Environments track had six interactive workshops that provided facilitated discussion and provide recommendations in the areas of: (1) Authentic project/problem identification in clinical, industrial, and global settings, (2) Experiential problem/project-based learning within courses, (3) Experiential learning in co-curricular learning settings, (4) Team-based learning, (5) Teaching to reach a diverse classroom, and (6) innovative platforms and pedagogy. A summary of the findings, best practices and recommendations from each of the workshops is provided under separate headings below, and a list of resources is provided at the end of this paper.

Cardiac segmentation by a velocity-aided active contour model.

Heart disease is one of the more life-threatening diseases. Accurate diagnosis and treatment are central to the survival of patients. Numerous diagnostic methods that can assess abnormalities of the heart have been developed. Among these methods, cardiac functional analysis has been widely used to derive cardiac functional parameters that describe the functionality of the heart and are frequently used in diagnosis of various heart diseases. Segmentation of the myocardial boundaries is an essential step for deriving these cardiac functional parameters, and the accuracy of parameters depends much on the correctness of the segmented boundaries. Therefore, it is essential that cardiac segmentation be accurate and reliable. However, current segmentation techniques still have difficulty both extracting accurate myocardial boundaries, especially the endocardial boundary and performing a fully automatic process because of low image quality, the complex shape and motion pattern of the heart, and lack of clear delineation between the myocardium and adjacent anatomic structures. A velocity-aided cardiac segmentation method based a modified active contour model, the tensor-based orientation gradient force (OGF) and phase contrast magnetic resonance imaging (MRI) has been developed to improve the accuracy of segmentation of the myocardial boundaries, especially the endocardial boundary. Furthermore, the initial seed contour tracking (SCT) algorithm has been also developed to improve the accuracy of automatic sequential frame segmentation in conjunction with the OGF-based segmentation method. The performance of the proposed method was assessed by experimentations on a phase contrast MRI data set of three normal human volunteer. The results of the individual frame segmentation showed that the accuracy and reproducibility of segmentation of the endocardial boundary by the use of the OGF was generally improved around the lower level of the LV and end systole. The results of the sequential frame segmentation showed that the propagation of errors caused was significantly reduced by the use of the SCT in addition to the OGF and improvements in the accuracy and reproducibility of segmentation of the endocardial boundary were much higher than the individual frame segmentation. However, improvements were generally negligible around the upper level of the LV and end diastole, and the velocity wrap-around problem and blood turbulence around the basal level of the ventricles even degraded the performance of boundary segmentation. Although this work demonstrates the potential of using the velocity information from phase contrast MRI for cardiac segmentation, the velocity wrap-around artifacts in phase contrast MRI data sets can degrade the performance. Therefore, future work must include the development of appropriate methods to cope with these artifacts.

Tracking retinal motion with a scanning laser ophthalmoscope.

The vast majority of people with low vision retain some functional vision to perform everyday tasks. To study the effectiveness and efficiency of the visual tasks performed by people with low vision, knowing the movement patterns of their preferred retinal locus (PRL) used for fixation, saccade, and pursuit is critical. The scanning laser ophthalmoscope (SLO) has been used to acquire retinal images while a subject is performing a visual tracking exercise. SLO data has traditionally been analyzed with the use of manual techniques that are both time-consuming and prone to errors due to operator fatigue. To improve the speed and accuracy of the analysis of retinal motion from SLO image sequences, we developed an automated image processing technique and tested it using MATLAB(TM) (The MathWorks, Natick, MA) software. The new software technique was experimentally tested on both normal- and low-vision subjects and compared with the results obtained using manual techniques. The findings indicate that the new technique works very well for most subjects, fairing poorly only in subjects where the quality of the SLO images was substandard.

Elastically deformable model-based motion-tracking of left ventricle.

The motion of the myocardium is a sensitive indicator of many types of heart disease. Quantitative characterization of this motion is essential for the accurate diagnosis and treatment of heart disease. Although several magnetic resonance imaging (MRI) techniques, such as tagged MRI and phase contrast MRI, provide noninvasive tools to obtain correlation of the position of points within the myocardium between images taken at subsequent time phases, the accurate tracking of the movement of these points remains a challenge due to the relatively low out-of-plane resolution of these imaging techniques. A motion tracking method based on elastic deformation estimation of a deformable model has been developed to track the three-dimensional motion of the myocardium. Elastic deformation estimation is performed on phase contrast MRI data by balancing the deformation potential energy of a deformable model and the potential energy derived from integrating velocity values of myocardial tissue points. The advantage of this method is that it can provide a physically plausible yet computationally efficient framework for cardiac motion tracking. To assess the proposed method, the motion of a normal human left ventricle (LV) was tracked throughout the entire cardiac cycle, and a quantitative strain analysis of the motion of the LV was carried out from end diastole to end systole. The results showed that the strain measurements were generally found to be consistent with previously published values.