Robust tracking of deformable anatomical structures with severe occlusions using deformable geometrical primitives.

Background And Objective: Surgical robotics tends to develop cognitive control architectures to provide certain degree of autonomy to improve patient safety and surgery outcomes, while decreasing the required surgeons' cognitive load dedicated to low level decisions. Cognition needs workspace perception, which is an essential step towards automatic decision-making and task planning capabilities. Robust and accurate detection and tracking in minimally invasive surgery suffers from limited visibility, occlusions, anatomy deformations and camera movements.

Robot assisted Fetoscopic Laser Coagulation: Improvements in navigation, re-location and coagulation.

Fetoscopic Laser Coagulation (FLC) for Twin to Twin Transfusion Syndrome is a challenging intervention due to the working conditions: low quality images acquired from a 3 mm fetoscope inside a turbid liquid environment, local view of the placental surface, unstable surgical field and delicate tissue layers. FLC is based on locating, coagulating and reviewing anastomoses over the placenta's surface. The procedure demands the surgeons to generate a mental map of the placenta with the distribution of the anastomoses, maintaining, at the same time, precision in coagulation and protecting the placenta and amniotic sac from potential damages.

Meshless Simulation of Multi-site Radio Frequency Catheter Ablation through the Fragile Points Method.

Computational models for radio frequency catheter ablation (RFCA) of cardiac arrhythmia have been developed and tested in conditions where a single ablation site is considered. However, in reality arrhythmic events are generated at multiple sites which are ablated during treatment. Under such conditions, heat accumulation from several ablations is expected and models should take this effect into account.

A Framework for the Evaluation of Human Machine Interfaces of Robot-Assisted Colonoscopy.

Unlabelled: The Human Machine Interface (HMI) of intraluminal robots has a crucial impact on the clinician's performance. It increases or decreases the difficulty of the tasks, and is connected to the users' physical and mental stress.

Objective: This article presents a framework to compare and evaluate different HMIs for robotic colonoscopy, with the objective of identifying the optimal HMI that minimises the clinician's effort and maximises the clinical outcomes.

EndoTrainer: a novel hybrid training platform for endoscopic surgery.

Purpose: Endoscopy implies high demanding procedures, and their practice requires structured formation curricula supported by adequate training platforms. Physical platforms are the most standardised solution for surgical training, but over the last few years, virtual platforms have been progressively introduced. This research work presents a new hybrid, physic-virtual, endoscopic training platform that exploits the benefits of the two kind of platforms combining realistic tools and phantoms together with the capacity of measuring all relevant parameters along the execution of the exercises and of providing an objective assessment performance.

Beat-to-beat fetal heart rate analysis using portable medical device and wavelet transformation technique.

Beat-to-beat tele-fetal monitoring and comparison with clinical data are studied with a wavelet transformation approach. Tele-fetal monitoring is a big progress toward a wearable medical device for pregnant women capable of obtaining prenatal care at home. We apply a wavelet transformation algorithm for fetal cardiac monitoring using a portable fetal Doppler medical device.

Training Simulators for Gastrointestinal Endoscopy: Current and Future Perspectives.

Gastrointestinal (GI) endoscopy is the gold standard in the detection and treatment of early and advanced GI cancers. However, conventional endoscopic techniques are technically demanding and require visual-spatial skills and significant hands-on experience. GI endoscopy simulators represent a valid solution to allow doctors to practice in a pre-clinical scenario.

Vision Based Robot Assistance in TTTS Fetal Surgery.

This paper presents an accurate and robust tracking vision algorithm for Fetoscopic Laser Photo-coagulation (FLP) surgery for Twin-Twin Transfusion Syndrome (TTTS). The aim of the proposed method is to assist surgeons during anastomosis localization, coagulation and review using a tele-operated robotic system. The algorithm computes the relative position of the fetoscope tool tip with respect to the placenta, via local vascular structure registration.

Sliding to predict: vision-based beating heart motion estimation by modeling temporal interactions.

Purpose: Technical advancements have been part of modern medical solutions as they promote better surgical alternatives that serve to the benefit of patients. Particularly with cardiovascular surgeries, robotic surgical systems enable surgeons to perform delicate procedures on a beating heart, avoiding the complications of cardiac arrest. This advantage comes with the price of having to deal with a dynamic target which presents technical challenges for the surgical system.

Robust cardiac motion estimation using ultrafast ultrasound data: a low-rank topology-preserving approach.

Cardiac motion estimation is an important diagnostic tool for detecting heart diseases and it has been explored with modalities such as MRI and conventional ultrasound (US) sequences. US cardiac motion estimation still presents challenges because of complex motion patterns and the presence of noise. In this work, we propose a novel approach to estimate cardiac motion using ultrafast ultrasound data.

Adaptive segmentation and mask-specific Sobolev inpainting of specular highlights for endoscopic images.

Minimally invasive surgical and diagnostic systems rely on endoscopic images of internal organs to assist medical tasks. Specular highlights are common on those images due to the strong reflectivity of the mucus layer on the organs and the relatively high intensity of the light source. This is a significant source of error that can affect the systems' performance.

Towards estimating cardiac motion using low-rank representation and topology preservation for ultrafast ultrasound data.

Estimation of the cardiac motion is very important in order to detect heart diseases. This work presents a cardiac motion estimation approach using ultrafast ultrasound data. We optimize a variational framework which has the benefits of combining low-rank data representation with topology preservation.

Towards Retrieving Force Feedback in Robotic-Assisted Surgery: A Supervised Neuro-Recurrent-Vision Approach.

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure.

Design and development of a microarray processing station (MPS) for automated miniaturized immunoassays.

Here we describe the design and evaluation of a fluidic device for the automatic processing of microarrays, called microarray processing station or MPS. The microarray processing station once installed on a commercial microarrayer allows automating the washing, and drying steps, which are often performed manually. The substrate where the assay occurs remains on place during the microarray printing, incubation and processing steps, therefore the addressing of nL volumes of the distinct immunoassay reagents such as capture and detection antibodies and samples can be performed on the same coordinate of the substrate with a perfect alignment without requiring any additional mechanical or optical re-alignment methods.

Compliant gait assistance triggered by user intention.

An automatic gait initialization strategy based on user intention sensing in the context of rehabilitation with a lower-limb wearable robot is proposed and evaluated. The proposed strategy involves monitoring the human-orthosis interaction torques and initial position deviation to determine the gait initiation instant and to modify orthosis operation for gait assistance, when needed. During gait, the compliant control algorithm relies on the adaptation of the joints' stiffness in function of their interaction torques and their deviation from the desired trajectories, while maintaining the dynamic stability.

Automatic and robust single-camera specular highlight removal in cardiac images.

In computer-assisted beating heart surgeries, accurate tracking of the heart's motion is of huge importance and there is a continuous need to eliminate any source of error that might disturb the tracking process. One source of error is the specular reflection that appears on the glossy surface of the heart. In this paper, we propose a robust solution for the detection and removal of specular highlights.

Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery.

The lack of force feedback is considered one of the major limitations in Robot Assisted Minimally Invasive Surgeries. Since add-on sensors are not a practical solution for clinical environments, in this paper we present a force estimation approach that starts with the reconstruction of a 3D deformation structure of the tissue surface by minimizing an energy functional. A Recurrent Neural Network-Long Short Term Memory (RNN-LSTM) based architecture is then presented to accurately estimate the applied forces.

Unconstrained ℓ1-regularized minimization with interpolated transformations for heart motion compensation.

Motion compensation constitutes a challenging issue in minimally invasive beating heart surgery. Since the zone to be repaired has a dynamic behaviour, precision and surgeon's dexterity decrease. In order to solve this problem, various proposals have been presented using ℓ2-norm.

Auto-adaptive robot-aided therapy using machine learning techniques.

This paper presents an application of a classification method to adaptively and dynamically modify the therapy and real-time displays of a virtual reality system in accordance with the specific state of each patient using his/her physiological reactions. First, a theoretical background about several machine learning techniques for classification is presented. Then, nine machine learning techniques are compared in order to select the best candidate in terms of accuracy.

Detection of movements with attention or distraction to the motor task during robot-assisted passive movements of the upper limb.

Robot-assisted rehabilitation therapies usually focus on physical aspects rather than on cognitive factors. However, cognitive aspects such as attention, motivation, and engagement play a critical role in motor learning and thus influence the long-term success of rehabilitation programs. This paper studies motor-related EEG activity during the execution of robot-assisted passive movements of the upper limb, while participants either: i) focused attention exclusively on the task; or ii) simultaneously performed another task.

Motor-model-based dynamic scaling in human-computer interfaces.

This paper presents a study on how the application of scaling techniques to an interface affects its performance. A progressive scaling factor based on the position and velocity of the cursor and the targets improves the efficiency of an interface, thereby reducing the user's workload. The study uses several human-motor models to interpret human intention and thus contribute to defining and adapting the scaling parameters to the execution of the task.