Real-time 3D Tracking of Articulated Tools for Robotic Surgery

In robotic surgery, tool tracking is important for providing safe tool-tissue interaction and facilitating surgical skills assessment. Despite recent advances in tool tracking, existing approaches are faced with major difficulties in real-time tracking of articulated tools. Most algorithms are tailored for offline processing with pre-recorded videos. In this paper, we propose a real-time 3D tracking method for articulated tools in robotic surgery. The proposed method is based on the CAD model of the tools as well as robot kinematics to generate online part-based templates for efficient 2D matching and 3D pose estimation. A robust verification approach is incorporated to reject outliers in 2D detections, which is then followed by fusing inliers with robot kinematic readings for 3D pose estimation of the tool. The proposed method has been validated with phantom data, as well as ex vivo and in vivo experiments. The results derived clearly demonstrate the performance advantage of the proposed method when compared to the state-of-the-art.Comment: This paper was presented in MICCAI 2016 conference, and a DOI was linked to the publisher's versio

Motility of small nematodes in disordered wet granular media

The motility of the worm nematode \textit{Caenorhabditis elegans} is investigated in shallow, wet granular media as a function of particle size dispersity and area density ($\phi$). Surprisingly, we find that the nematode's propulsion speed is enhanced by the presence of particles in a fluid and is nearly independent of area density. The undulation speed, often used to differentiate locomotion gaits, is significantly affected by the bulk material properties of wet mono- and polydisperse granular media for $\phi \geq 0.55$. This difference is characterized by a change in the nematode's waveform from swimming to crawling in dense polydisperse media \textit{only}. This change highlights the organism's adaptability to subtle differences in local structure and response between monodisperse and polydisperse media

Concurrent Segmentation and Localization for Tracking of Surgical Instruments

Real-time instrument tracking is a crucial requirement for various computer-assisted interventions. In order to overcome problems such as specular reflections and motion blur, we propose a novel method that takes advantage of the interdependency between localization and segmentation of the surgical tool. In particular, we reformulate the 2D instrument pose estimation as heatmap regression and thereby enable a concurrent, robust and near real-time regression of both tasks via deep learning. As demonstrated by our experimental results, this modeling leads to a significantly improved performance than directly regressing the tool position and allows our method to outperform the state of the art on a Retinal Microsurgery benchmark and the MICCAI EndoVis Challenge 2015.Comment: I. Laina and N. Rieke contributed equally to this work. Accepted to MICCAI 201

Cut Points and Diffusions in Random Environment

In this article we investigate the asymptotic behavior of a new class of multi-dimensional diffusions in random environment. We introduce cut times in the spirit of the work done by Bolthausen, Sznitman and Zeitouni, see [4], in the discrete setting providing a decoupling effect in the process. This allows us to take advantage of an ergodic structure to derive a strong law of large numbers with possibly vanishing limiting velocity and a central limit theorem under the quenched measure.Comment: 44 pages; accepted for publication in "Journal of Theoretical Probability

Random walks in random Dirichlet environment are transient in dimension d≥3d\ge 3

We consider random walks in random Dirichlet environment (RWDE) which is a special type of random walks in random environment where the exit probabilities at each site are i.i.d. Dirichlet random variables. On $\Z^d$, RWDE are parameterized by a $2d$-uplet of positive reals. We prove that for all values of the parameters, RWDE are transient in dimension $d\ge 3$. We also prove that the Green function has some finite moments and we characterize the finite moments. Our result is more general and applies for example to finitely generated symmetric transient Cayley graphs. In terms of reinforced random walks it implies that directed edge reinforced random walks are transient for $d\ge 3$.Comment: New version published at PTRF with an analytic proof of lemma

Simultaneous recognition and pose estimation of instruments in minimally invasive surgery

Detection of surgical instruments plays a key role in ensuring patient safety in minimally invasive surgery. In this paper, we present a novel method for 2D vision-based recognition and pose estimation of surgical instruments that generalizes to different surgical applications. At its core, we propose a novel scene model in order to simultaneously recognize multiple instruments as well as their parts. We use a Convolutional Neural Network architecture to embody our model and show that the cross-entropy loss is well suited to optimize its parameters which can be trained in an end-to-end fashion. An additional advantage of our approach is that instrument detection at test time is achieved while avoiding the need for scale-dependent sliding window evaluation. This allows our approach to be relatively parameter free at test time and shows good performance for both instrument detection and tracking. We show that our approach surpasses state-of-the-art results on in-vivo retinal microsurgery image data, as well as ex-vivo laparoscopic sequences

Visualization of respiratory flows from 3D reconstructed alveolar airspaces using X-ray tomographic microscopy

A deeper knowledge of the three-dimensional (3D) structure of the pulmonary acinus has direct applications in studies on acinar fluid dynamics and aerosol kinematics. To date, however, acinar flow simulations have been often based on geometrical models inspired by morphometrical studies; limitations in the spatial resolution of lung imaging techniques have prevented the simulation of acinar flows using 3D reconstructions of such small structures. In the present study, we use high-resolution, synchrotron radiation-based X-ray tomographic microscopy (SRXTM) images of the pulmonary acinus of a mouse to reconstruct 3D alveolar airspaces and conduct computational fluid dynamic (CFD) simulations mimicking rhythmic breathing motion. Respiratory airflows and Lagrangian (massless) particle tracking are visualized in two examples of acinar geometries with varying size and complexity, representative of terminal sacculi including their alveoli. The present CFD simulations open the path towards future acinar flow and aerosol deposition studies in complete and anatomically realistic multi-generation acinar trees using reconstructed 3D SRXTM geometries

Slow movement of a random walk on the range of a random walk in the presence of an external field

In this article, a localisation result is proved for the biased random walk on the range of a simple random walk in high dimensions (d \geq 5). This demonstrates that, unlike in the supercritical percolation setting, a slowdown effect occurs as soon a non-trivial bias is introduced. The proof applies a decomposition of the underlying simple random walk path at its cut-times to relate the associated biased random walk to a one-dimensional random walk in a random environment in Sinai's regime

Ground State Energy of the One-Dimensional Discrete Random Schr\"{o}dinger Operator with Bernoulli Potential

In this paper, we show the that the ground state energy of the one dimensional Discrete Random Schroedinger Operator with Bernoulli Potential is controlled asymptotically as the system size N goes to infinity by the random variable \ell_N, the length the longest consecutive sequence of sites on the lattice with potential equal to zero. Specifically, we will show that for almost every realization of the potential the ground state energy behaves asymptotically as $\frac{\pi^2}{\ell_N+1)^2}$ in the sense that the ratio of the quantities goes to one

Propulsive Force Measurements and Flow Behavior of Undulatory Swimmers at Low Reynolds Number

The swimming behavior of the nematode Caenorhabditis elegans is investigated in aqueous solutions of increasing viscosity. Detailed flow dynamics associated with the nematode’s swimming motion as well as propulsive force and power are obtained using particle tracking and velocimetry methods. We find that C. elegans delivers propulsive thrusts on the order of a few nanonewtons. Such findings are supported by values obtained using resistive force theory; the ratio of normal to tangential drag coefficients is estimated to be approximately 1.4. Over the range of solutions investigated here, the flow properties remain largely independent of viscosity. Velocity magnitudes of the flow away from the nematode body decay rapidly within less than a body length and collapse onto a single master curve. Overall, our findings support that C. elegans is an attractive living model to study the coupling between small-scale propulsion and low Reynolds number hydrodynamics