Objective-The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the preoperative MR data and the intraoperative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI.Materials and Methods-Eleven consecutive patients with supratentorial gliomas were included in our study. All underwent surgery at our intra-operative MR imaging-guided therapy facility and have tumors in eloquent brain areas (e.g. precentral gyrus and cortico-spinal tract). Functional MRI and DT-MRI, together with MPRAGE and T2w structural MRI were acquired at 3T prior to surgery. SPGR and T2w images were acquired with a 0.5T magnet during each procedure. Quantitative assessment of the alignment accuracy was carried out and compared with current state-of the-art systems based only on rigid-registration.Results-Alignment between preoperative and intra-operative datasets was successfully carried out during surgery for all patients. Overall, the mean residual displacement remaining after nonrigid registration was 1.82 mm. There is a statistically significant improvement in alignment accuracy utilizing our non-rigid registration in comparison to the currently used technology (p<0.001).Conclusions-We were able to achieve intra-operative rigid and non-rigid registration of (1) pre-operative structural MRI with intra-operative T1w MRI; (2) pre-operative FMRI with intraoperative T1w MRI, and (3) pre-operative DT-MRI with intra-operative T1w MRI. The registration algorithms as implemented were sufficiently robust and rapid to meet the hard realtime constraints of intra-operative surgical decision making. The validation experiments demonstrate that we can accurately compensate for the deformation of the brain and thus can construct an augmented reality visualization to aid the surgeon.

<p>Online video lectures have been considered an instructional media for various pedagogic approaches, such as the flipped classroom and open online courses. In comparison to other instructional media, online video affords the opportunity for recording student clickstream patterns within a video lecture. Video analytics within lecture videos may provide insights into student learning performance and inform the improvement of video-assisted teaching tactics. Nevertheless, video analytics are not accessible to learning stakeholders, such as researchers and educators, mainly because online video platforms do not broadly share the interactions of the users with their systems. For this purpose, we have designed an open-access video analytics system for use in a video-assisted course. In this paper, we present a longitudinal study, which provides valuable insights through the lens of the collected video analytics. In particular, we found that there is a relationship between video navigation (repeated views) and the level of cognition/thinking required for a specific video segment. Our results indicated that learning performance progress was slightly improved and stabilized after the third week of the video-assisted course. We also found that attitudes regarding easiness, usability, usefulness, and acceptance of this type of course remained at the same levels throughout the course. Finally, we triangulate analytics from diverse sources, discuss them, and provide the lessons learned for further development and refinement of video-assisted courses and practices.</p>

The paper proposes a new measure for the cohesion of classes in Object-Oriented software systems. It is based on the analysis of latent topics embedded in comments and identifiers in source code. The measure, named as Maximal Weighted Entropy, utilizes the Latent Dirichlet Allocation technique and information entropy measures to quantitatively evaluate the cohesion of classes in software. This paper presents the principles and the technology that stand behind the proposed measure. Two case studies on a large open source software system are presented. They compare the new measure with an extensive set of existing metrics and use them to construct models that predict software faults. The case studies indicate that the novel measure captures different aspects of class cohesion compared to the existing cohesion measures and improves fault prediction for most metrics, which are combined with Maximal Weighted Entropy. 978-1-4244-4828-9/09/$25.00 2009 IEEE Proc. ICSM 2009, Edmonton, Canada