E-DQN-Based Path Planning Method for Drones in Airsim Simulator under Unknown Environment.

To improve the rapidity of path planning for drones in unknown environments, a new bio-inspired path planning method using E-DQN (event-based deep -network), referring to introducing event stream to reinforcement learning network, is proposed. Firstly, event data are collected through an airsim simulator for environmental perception, and an auto-encoder is presented to extract data features and generate event weights. Then, event weights are input into DQN (deep -network) to choose the action of the next step.

Brain inspired path planning algorithms for drones.

Introduction: With the development of artificial intelligence and brain science, brain-inspired navigation and path planning has attracted widespread attention.

Methods: In this paper, we present a place cell based path planning algorithm that utilizes spiking neural network (SNN) to create efficient routes for drones. First, place cells are characterized by the leaky integrate-and-fire (LIF) neuron model.

Benchmarking Highly Parallel Hardware for Spiking Neural Networks in Robotics.

Animal brains still outperform even the most performant machines with significantly lower speed. Nonetheless, impressive progress has been made in robotics in the areas of vision, motion- and path planning in the last decades. Brain-inspired Spiking Neural Networks (SNN) and the parallel hardware necessary to exploit their full potential have promising features for robotic application.

Embodied Synaptic Plasticity With Online Reinforcement Learning.

The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in isolation on pattern classification tasks. This contrasts with the biological brain which purpose is to control a body in closed-loop.

Generating Pointing Motions for a Humanoid Robot by Combining Motor Primitives.

The human motor system is robust, adaptive and very flexible. The underlying principles of human motion provide inspiration for robotics. Pointing at different targets is a common robotics task, where insights about human motion can be applied.

Neuromorphic Stereo Vision: A Survey of Bio-Inspired Sensors and Algorithms.

Any visual sensor, whether artificial or biological, maps the 3D-world on a 2D-representation. The missing dimension is depth and most species use stereo vision to recover it. Stereo vision implies multiple perspectives and matching, hence it obtains depth from a pair of images.

Computer-assisted 3D bowel length measurement for quantitative laparoscopy.

Background: This study aimed at developing and evaluating a tool for computer-assisted 3D bowel length measurement (BMS) to improve objective measurement in minimally invasive surgery. Standardization and quality of surgery as well as its documentation are currently limited by lack of objective intraoperative measurements. To solve this problem, we developed BMS as a clinical application of Quantitative Laparoscopy (QL).

Scaling up liquid state machines to predict over address events from dynamic vision sensors.

Short-term visual prediction is important both in biology and robotics. It allows us to anticipate upcoming states of the environment and therefore plan more efficiently. In theoretical neuroscience, liquid state machines have been proposed as a biologically inspired method to perform asynchronous prediction without a model.

Projective biomechanical depth matching for soft tissue registration in laparoscopic surgery.

Purpose: A key component of computer- assisted surgery systems is the accurate and robust registration of preoperative planning data with intraoperative sensor data. In laparoscopic surgery, this image-based registration remains challenging due to soft tissue deformations. This paper presents a novel approach for biomechanical soft tissue registration of preoperative CT data with stereo endoscopic image data.

Mycolactone subverts immunity by selectively blocking the Sec61 translocon.

Mycolactone, an immunosuppressive macrolide released by the human pathogen Mycobacterium ulcerans, was previously shown to impair Sec61-dependent protein translocation, but the underlying molecular mechanism was not identified. In this study, we show that mycolactone directly targets the α subunit of the Sec61 translocon to block the production of secreted and integral membrane proteins with high potency. We identify a single-amino acid mutation conferring resistance to mycolactone, which localizes its interaction site near the lumenal plug of Sec61α.

Bridging the gap between formal and experience-based knowledge for context-aware laparoscopy.

Purpose: Computer assistance is increasingly common in surgery. However, the amount of information is bound to overload processing abilities of surgeons. We propose methods to recognize the current phase of a surgery for context-aware information filtering.

Intraoperative on-the-fly organ-mosaicking for laparoscopic surgery.

The goal of computer-assisted surgery is to provide the surgeon with guidance during an intervention, e.g., using augmented reality.

Toward cognitive pipelines of medical assistance algorithms.

Purpose: Assistance algorithms for medical tasks have great potential to support physicians with their daily work. However, medicine is also one of the most demanding domains for computer-based support systems, since medical assistance tasks are complex and the practical experience of the physician is crucial. Recent developments in the area of cognitive computing appear to be well suited to tackle medicine as an application domain.

Image-based laparoscopic bowel measurement.

Purpose: Minimally invasive interventions offer benefits for patients, while also entailing drawbacks for surgeons, such as the loss of depth perception. Thus estimating distances, which is of particular importance in gastric bypasses, becomes difficult. In this paper, we propose an approach based on stereo endoscopy that segments organs on-the-fly and measures along their surface during a minimally invasive interventions.

LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition.

Purpose: The rise of intraoperative information threatens to outpace our abilities to process it. Context-aware systems, filtering information to automatically adapt to the current needs of the surgeon, are necessary to fully profit from computerized surgery. To attain context awareness, representation of medical knowledge is crucial.

OpenHELP (Heidelberg laparoscopy phantom): development of an open-source surgical evaluation and training tool.

Background: Apart from animal testing and clinical trials, surgical research and laparoscopic training mainly rely on phantoms. The aim of this project was to design a phantom with realistic anatomy and haptic characteristics, modular design and easy reproducibility. The phantom was named open-source Heidelberg laparoscopic phantom (OpenHELP) and serves as an open-source platform.

Noninvasive 4D pressure difference mapping derived from 4D flow MRI in patients with repaired aortic coarctation: comparison with young healthy volunteers.

To assess spatial and temporal pressure characteristics in patients with repaired aortic coarctation compared to young healthy volunteers using time-resolved velocity-encoded three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI) and derived 4D pressure difference maps. After in vitro validation against invasive catheterization as gold standard, 4D flow MRI of the thoracic aorta was performed at 1.5T in 13 consecutive patients after aortic coarctation repair without recoarctation and 13 healthy volunteers.

Physics-based shape matching for intraoperative image guidance.

Purpose: Soft-tissue deformations can severely degrade the validity of preoperative planning data during computer assisted interventions. Intraoperative imaging such as stereo endoscopic, time-of-flight or, laser range scanner data can be used to compensate these movements. In this context, the intraoperative surface has to be matched to the preoperative model.

Noninvasive pressure difference mapping derived from 4D flow MRI in patients with unrepaired and repaired aortic coarctation.

Purpose: To develop a method for computing and visualizing pressure differences derived from time-resolved velocity-encoded three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI) and to compare pressure difference maps of patients with unrepaired and repaired aortic coarctation to young healthy volunteers.

Methods: 4D flow MRI data of four patients with aortic coarctation either before or after repair (mean age 17 years, age range 3-28, one female, three males) and four young healthy volunteers without history of cardiovascular disease (mean age 24 years, age range 20-27, one female, three males) was acquired using a 1.5-T clinical MR scanner.

A system for context-aware intraoperative augmented reality in dental implant surgery.

Purpose: Large volumes of information in the OR are ignored by surgeons when the amount outpaces human mental processing abilities. We developed an augmented reality (AR) system for dental implant surgery that acts as an automatic information filter, selectively displaying only relevant information. The purpose is to reduce information overflow and offer intuitive image guidance.

Towards open-source, low-cost haptics for surgery simulation.

In minimally invasive surgery (MIS), virtual reality (VR) training systems have become a promising education tool. However, the adoption of these systems in research and clinical settings is still limited by the high costs of dedicated haptics hardware for MIS. In this paper, we present ongoing research towards an open-source, low-cost haptic interface for MIS simulation.

The medical simulation markup language - simplifying the biomechanical modeling workflow.

Modeling and simulation of the human body by means of continuum mechanics has become an important tool in diagnostics, computer-assisted interventions and training. This modeling approach seeks to construct patient-specific biomechanical models from tomographic data. Usually many different tools such as segmentation and meshing algorithms are involved in this workflow.