Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment

This paper describes a scalable active learning pipeline prototype for large-scale brain mapping that leverages high performance computing power. It enables high-throughput evaluation of algorithm results, which, after human review, are used for iterative machine learning model training. Image processing and machine learning are performed in a batch layer. Benchmark testing of image processing using pMATLAB shows that a 100$\times$ increase in throughput (10,000%) can be achieved while total processing time only increases by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust scalability. The images and algorithm results are provided through a serving layer to a browser-based user interface for interactive review. This pipeline has the potential to greatly reduce the manual annotation burden and improve the overall performance of machine learning-based brain mapping.Comment: 6 pages, 5 figures, submitted to IEEE HPEC 2020 proceeding

X-Optogenetics and U-Optogenetics: Feasibility and Possibilities

Optogenetics is an established technique that uses visible light to modulate membrane voltage in neural cells. Although optogenetics allows researchers to study parts of the brain like never before, it is limited because it is invasive, and visible light cannot travel very deeply into tissue. This paper proposes two new techniques that remedy these challenges. The first is x-optogenetics, which uses visible light-emitting nanophosphors stimulated by focused x-rays. X-rays can penetrate much more deeply than infrared light and allow for nerve cell stimulation in any part of the brain. The second is u-optogenetics, which is an application of sonoluminescence to optogenetics. Such a technique uses ultrasound waves instead of x-rays to induce light emission, so there would be no introduction of radiation. However, the tradeoff is that the penetration depth of ultrasound is less than that of x-ray. The key issues affecting feasibility are laid out for further investigation into both x-optogenetics and u-optogenetics

AI-Enabled, Ultrasound-Guided Handheld Robotic Device for Femoral Vascular Access

Hemorrhage is a leading cause of trauma death, particularly in prehospital environments when evacuation is delayed. Obtaining central vascular access to a deep artery or vein is important for administration of emergency drugs and analgesics, and rapid replacement of blood volume, as well as invasive sensing and emerging life-saving interventions. However, central access is normally performed by highly experienced critical care physicians in a hospital setting. We developed a handheld AI-enabled interventional device, AI-GUIDE (Artificial Intelligence Guided Ultrasound Interventional Device), capable of directing users with no ultrasound or interventional expertise to catheterize a deep blood vessel, with an initial focus on the femoral vein. AI-GUIDE integrates with widely available commercial portable ultrasound systems and guides a user in ultrasound probe localization, venous puncture-point localization, and needle insertion. The system performs vascular puncture robotically and incorporates a preloaded guidewire to facilitate the Seldinger technique of catheter insertion. Results from tissue-mimicking phantom and porcine studies under normotensive and hypotensive conditions provide evidence of the technique’s robustness, with key performance metrics in a live porcine model including: a mean time to acquire femoral vein insertion point of 53 ± 36 s (5 users with varying experience, in 20 trials), a total time to insert catheter of 80 ± 30 s (1 user, in 6 trials), and a mean number of 1.1 (normotensive, 39 trials) and 1.3 (hypotensive, 55 trials) needle insertion attempts (1 user). These performance metrics in a porcine model are consistent with those for experienced medical providers performing central vascular access on humans in a hospital