We propose a convolutional neural network (CNN) aided factor graphs assisted by mutual information features estimated by a neural network for seizure detection. Specifically, we use neural mutual information estimation to evaluate the correlation between different electroencephalogram (EEG) channels as features. We then use a 1D-CNN to extract extra features from the EEG signals and use both features to estimate the probability of a seizure event. Finally, learned factor graphs are employed to capture the temporal correlation in the signal. Both sets of features from the neural mutual estimation and the 1D-CNN are used to learn the factor nodes. We show that the proposed method achieves state-of-the-art performance using 6-fold leave-four-patients-out cross-validation.

Training and planning for neurosurgeries necessitate many requirements from junior neurosurgeons, including perceptual capacities. An effective method of deliberate training is to replicate the required procedures using neurosurgical simulation tools and visualizing a three-dimensional (3D) workspace. However, Augmented Reality (AR) neurosurgical simulators become obsolete for a variety of reasons, including users' distance underestimation. Few investigations have been conducted for improving users' depth perception in AR systems with perceptual motion cues through neurosurgical simulation tools for planning aid purposes. In this poster, we are reporting a user study about whether head motion and perceptual motion cues have any an influence on users' depth perception.

Preterm neonates with a very low birth weight of less than 1,500 grams are at increased risk for developing intraventricular hemorrhage (IVH), which is a major cause of brain injury in preterm neonates. Quantitative measurements of ventricular dilatation or shrinkage play an important role in monitoring patients and evaluating treatment options. 3D ultrasound (US) has been developed to monitor ventricle volume as a biomarker for ventricular changes. However, ventricle volume as a global indicator does not allow for precise analysis of local ventricular changes, which could be linked to specific neurological problems often seen in the patient population later in life. In this work, a 3D+t spatial-temporal deformable registration approachis proposed, which is applied to the analysis of the detailed local changes of preterm IVH neonatal ventricles from 3D US images. In particular, a novel sequential convex/dual optimization algorithm is introduced to extract the optimal 3D+t spatial-temporal deformable field, which simultaneously optimizes the sequence of 3D deformation fieldswhile enjoying both efficiencyand simplicity in numerics. The developed registration technique was evaluated by comparing two manually extracted ventricle surfaces from the baseline and the registered follow-up images using the metrics of Dice similarity coefficient (DSC), mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD). The performed experiments using 14 patients with 5 time-point images per patient show that the proposed 3D+t registration approach accurately recovered the longitudinal deformation of ventricle surfaces from 3D US images. The proposed approach may be potentially used to analyse the change pattern of cerebral ventricles of IVH patients, their response to different treatment options, and to elucidate the deficiencies that a patient could have later in life. To the best of our knowledge, this paper reports the first study on the longitudinalanalysis of neonatal ventricular system from 3D US images.

We present a prototype of a system in development for pre-operative planning. The proposed NeuroTable uses a combination of traditional rendering and novel visualization techniques rendered to facilitate real-time collaboration between neurosurgeons during intervention planning. A set of multimodal 2D and 3D renderings convey the relation between the region of interest and the surrounding anatomical structures. A haptic device is used for interaction with the NeuroTable to facilitate immersive control over the 3D cursor and navigation modes for the neurosurgeons during their discourse of planning. A pilot experimental study was conducted to assess the performance of users in targeting points within the preoperative 3D scan. Then, two clinicians participated in the evaluation of the table in discussing and planning a case. Results indicate that the NeuroTable facilitated the discourse and we discuss the results of the speed and accuracy for the specification of entry and target points.

The placement of an external ventricular drain is one of the most commonly performed neurosurgical procedures, and consequently, is an essential skill to be mastered by neurosurgical trainees. The optimal placement of the drain involves choosing an appropriate burr hole on the skull and blindly placing a catheter through the burr hole to intersect a lateral ventricle in order to drain cerebrospinal fluid and relieve intracranial pressure. Undesirable trajectories lead to multiple tries in order to hit the ventricle, with potential risk of damaging eloquent brain areas. In this paper, we describe the development of a simulation environment to train residents on the acquisition of these targeting skills before attempting the placement on live patients. The platform is coupled with an augmented reality image-guidance tool, developed in our lab, to help with the visualization of the ventricles in the patient's head. Performance is evaluated using Fitts' methodology (Fitts, 1954), which respects the users ability to trade-off speed and accuracy.

The primary purpose of medical image visualization is to improve patient outcomes by facilitating the inspection, analysis, and interpretation of patient data. This is only possible if the users’ perceptual and cognitive limitations are taken into account during every step of design, implementation, and evaluation of interactive displays. Visualization of medical images, if executed effectively and efficiently, can empower physicians to explore patient data rapidly and accurately with minimal cognitive effort. This article describes a specific case study in biomedical visualization system design and evaluation, which is the visualization of MR angiography images for planning arteriovenous malformation (AVM) interventions. The success of an AVM intervention greatly depends on the surgeon gaining a full understanding of the anatomy of the malformation and its surrounding structures. Accordingly, the purpose of this study was to investigate the usability of visualization modalities involving contour enhancement and stereopsis in the identification and localization of vascular structures using objective user studies. Our preliminary results indicate that contour enhancement, particularly when combined with stereopsis, results in improved performance enhancement of the perception of connectivity and relative depth between different structures.

We present a unique simulator-based methodology for assessing both technical and nontechnical (cognitive) skills for surgical trainees while immersed in a complete medical simulation environment. Further, we have included two crisis scenarios which allow for the evaluation of the effect of cognitive strategy selection on the low-level surgical skills. Training these mixed-mode scenarios can thereby be evaluated on our platform, allowing for improved assessment and a stronger foundation for credentialing, with the potential to reduce the occurrence of adverse events in the operating room. Scientific evaluation and validation of our work is conducted together with 19 junior surgeons in order to achieve the following goals: 1) to provide a qualitative measure of usability, 2) to assess vertebroplasty technical performance of the surgeon, and 3) to explore the relationship between mental workload and surgical performance during crisis. Our results indicate that: 1) the surgeons scored the face validity of our modeled simulation environment very highly ( 4.68 ±0.48, using a 5-point Likert scale), 2) surgeon training enabled completion of tasks more quickly, and 3) the introduction of crisis scenarios negatively affected the surgeons' objective performance. Taken together, our results underscore the need to develop realistic simulation environments that prepare young residents to respond to emergent events in the operating room.

Acquisition and fusion of multiple imaging modalities is becoming an increasingly desired clinical practice. This is particularly the case in radiation therapy, where dosage must be determined using electron density calculations from CT images, which lack the contrast to resolve soft-tissue structures. This often necessitates the fusion of corresponding MRI images in order to plot radiation trajectories safely around critical tissues. Simultaneous visualization of multiple volumes using direct volume rendering (DVR) techniques offers a number of advantages over traditional visualization methods. Specifically, fused visualization enhances the relational aspects of volumes, providing improved context [cite the first one]. However, there are many challenges involved in implementing DVR using fused data sets. The primary challenge is determining how images overlap to provide meaningful information. Additionally, there is increased computational complexity beyond standard DVR techniques, threatening real-time applications of fused DVR. These difficulties are evident to users of such systems as they must manage complex user interfaces and poor application performance. In this work, we introduce a user-centric multi-volume DVR technique which addresses issues of performance and ease of use. Through an intuitive interface, users are able to spatially define regions of interest, determining the relative contributions of each modality in the output rendering.