A computational fluid dynamics approach to determine white matter permeability

Glioblastomas represent a challenging problem with an extremely poor survival rate. Since these tumour cells have a highly invasive character, an effective surgical resection as well as chemotherapy and radiotherapy is very difficult. Convection-enhanced delivery (CED), a technique that consists in the injection of a therapeutic agent directly into the parenchyma, has shown encouraging results. Its efficacy depends on the ability to predict, in the pre-operative phase, the distribution of the drug inside the tumour. This paper proposes a method to compute a fundamental parameter for CED modelling outcomes, the hydraulic permeability, in three brain structures. Therefore, a bidimensional brain-like structure was built out of the main geometrical features of the white matter: axon diameter distribution extrapolated from electron microscopy images, extracellular space (ECS) volume fraction and ECS width. The axons were randomly allocated inside a defined border, and the ECS volume fraction as well as the ECS width maintained in a physiological range. To achieve this result, an outward packing method coupled with a disc shrinking technique was implemented. The fluid flow through the axons was computed by solving Navier–Stokes equations within the computational fluid dynamics solver ANSYS. From the fluid and pressure fields, an homogenisation technique allowed establishing the optimal representative volume element (RVE) size. The hydraulic permeability computed on the RVE was found in good agreement with experimental data from the literature

On the Value of Estimating Human Arm Stiffness during Virtual Teleoperation with Robotic Manipulators

Teleoperated robotic systems are widely spreading in multiple different fields, from hazardous environments exploration to surgery. In teleoperation, users directly manipulate a master device to achieve task execution at the slave robot side; this interaction is fundamental to guarantee both system stability and task execution performance. In this work, we propose a non-disruptive method to study the arm endpoint stiffness. We evaluate how users exploit the kinetic redundancy of the arm to achieve stability and precision during the execution of different tasks with different master devices. Four users were asked to perform two planar trajectories following virtual tasks using both a serial and a parallel link master device. Users' arm kinematics and muscular activation were acquired and combined with a user-specific musculoskeletal model to estimate the joint stiffness. Using the arm kinematic Jacobian, the arm end-point stiffness was derived. The proposed non-disruptive method is capable of estimating the arm endpoint stiffness during the execution of virtual teleoperated tasks. The obtained results are in accordance with the existing literature in human motor control and show, throughout the tested trajectory, a modulation of the arm endpoint stiffness that is affected by task characteristics and hand speed and acceleration

Towards realistic laparoscopic image generation using image-domain translation

Background and ObjectivesOver the last decade, Deep Learning (DL) has revolutionized data analysis in many areas, including medical imaging. However, there is a bottleneck in the advancement of DL in the surgery field, which can be seen in a shortage of large-scale data, which in turn may be attributed to the lack of a structured and standardized methodology for storing and analyzing surgical images in clinical centres. Furthermore, accurate annotations manually added are expensive and time consuming. A great help can come from the synthesis of artificial images; in this context, in the latest years, the use of Generative Adversarial Neural Networks (GANs) achieved promising results in obtaining photo-realistic images. MethodsIn this study, a method for Minimally Invasive Surgery (MIS) image synthesis is proposed. To this aim, the generative adversarial network pix2pix is trained to generate paired annotated MIS images by transforming rough segmentation of surgical instruments and tissues into realistic images. An additional regularization term was added to the original optimization problem, in order to enhance realism of surgical tools with respect to the background. Results Quantitative and qualitative (i.e., human-based) evaluations of generated images have been carried out in order to assess the effectiveness of the method. ConclusionsExperimental results show that the proposed method is actually able to translate MIS segmentations to realistic MIS images, which can in turn be used to augment existing data sets and help at overcoming the lack of useful images; this allows physicians and algorithms to take advantage from new annotated instances for their training

Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy

Objective: Surgical data science is evolving into a research field that aims to observe everything occurring within and around the treatment process to provide situation-aware data-driven assistance. In the context of endoscopic video analysis, the accurate classification of organs in the field of view of the camera proffers a technical challenge. Herein, we propose a new approach to anatomical structure classification and image tagging that features an intrinsic measure of confidence to estimate its own performance with high reliability and which can be applied to both RGB and multispectral imaging (MI) data. Methods: Organ recognition is performed using a superpixel classification strategy based on textural and reflectance information. Classification confidence is estimated by analyzing the dispersion of class probabilities. Assessment of the proposed technology is performed through a comprehensive in vivo study with seven pigs. Results: When applied to image tagging, mean accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB) and 96% (MI) with the confidence measure. Conclusion: Results showed that the confidence measure had a significant influence on the classification accuracy, and MI data are better suited for anatomical structure labeling than RGB data. Significance: This work significantly enhances the state of art in automatic labeling of endoscopic videos by introducing the use of the confidence metric, and by being the first study to use MI data for in vivo laparoscopic tissue classification. The data of our experiments will be released as the first in vivo MI dataset upon publication of this paper.Comment: 7 pages, 6 images, 2 table

Accurate multi-robot targeting for keyhole neurosurgery based on external sensors monitoring

Robotics has recently been introduced in surgery to improve intervention accuracy, to reduce invasiveness and to allow new surgical procedures. In this framework, the ROBOCAST system is an optically surveyed multi-robot chain aimed at enhancing the accuracy of surgical probe insertion during keyhole neurosurgery procedures. The system encompasses three robots, connected as a multiple kinematic chain (serial and parallel), totalling 13 degrees of freedom, and it is used to automatically align the probe onto a desired planned trajectory. The probe is then inserted in the brain, towards the planned target, by means of a haptic interface. This paper presents a new iterative targeting approach to be used in surgical robotic navigation, where the multi-robot chain is used to align the surgical probe to the planned pose, and an external sensor is used to decrease the alignment errors. The iterative targeting was tested in an operating room environment using a skull phantom, and the targets were selected on magnetic resonance images. The proposed targeting procedure allows about 0.3 mm to be obtained as the residual median Euclidean distance between the planned and the desired targets, thus satisfying the surgical accuracy requirements (1 mm), due to the resolution of the diffused medical images. The performances proved to be independent of the robot optical sensor calibration accuracy