SERV-CT: A disparity dataset from cone-beam CT for validation of endoscopic 3D reconstruction

In computer vision, reference datasets from simulation and real outdoor scenes have been highly successful in promoting algorithmic development in stereo reconstruction. Endoscopic stereo reconstruction for surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surface properties and the presence of blood and smoke. These issues present difficulties for both stereo reconstruction itself and also for standardised dataset production. Previous datasets have been produced using computed tomography (CT) or structured light reconstruction on phantom or ex vivo models. We present a stereo-endoscopic reconstruction validation dataset based on cone-beam CT (SERV-CT). Two ex vivo small porcine full torso cadavers were placed within the view of the endoscope with both the endoscope and target anatomy visible in the CT scan. Subsequent orientation of the endoscope was manually aligned to match the stereoscopic view and benchmark disparities, depths and occlusions are calculated. The requirement of a CT scan limited the number of stereo pairs to 8 from each ex vivo sample. For the second sample an RGB surface was acquired to aid alignment of smooth, featureless surfaces. Repeated manual alignments showed an RMS disparity accuracy of around 2 pixels and a depth accuracy of about 2 mm. A simplified reference dataset is provided consisting of endoscope image pairs with corresponding calibration, disparities, depths and occlusions covering the majority of the endoscopic image and a range of tissue types, including smooth specular surfaces, as well as significant variation of depth. We assessed the performance of various stereo algorithms from online available repositories. There is a significant variation between algorithms, highlighting some of the challenges of surgical endoscopic images. The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths covering the majority of the endoscopic image. This complements existing resources well and we hope will aid the development of surgical endoscopic anatomical reconstruction algorithms

Dominant g(9/2)^2 neutron configuration in the 4+1 state of 68Zn based on new g factor measurements

The $g$ factor of the $4_1^+$ state in $^{68}$Zn has been remeasured with improved energy resolution of the detectors used. The value obtained is consistent with the previous result of a negative $g$ factor thus confirming the dominant $0g_{9/2}$ neutron nature of the $4_1^+$ state. In addition, the accuracy of the $g$ factors of the $2_1^+$, $2_2^+$ and $3_1^-$ states has been improved an d their lifetimes were well reproduced. New large-scale shell model calculations based on a $^{56}$Ni core and an $0f_{5/2}1pg_{9/2}$ model space yield a theoretical value, $g(4_1^+) = +0.008$. Although the calculated value is small, it cannot fully explain the experimental value, $g(4_1^+) = -0.37(17)$. The magnitude of the deduced B(E2) of the $4_1^+$ and $2_1^+$ transition is, however, rather well described. These results demonstrate again the importance of $g$ factor measurements for nuclear structure determination s due to their specific sensitivity to detailed proton and neutron components in the nuclear wave functions.Comment: 7 pages, 3 figs, submitted to PL

Performance stability of preferred-oriented vapor-deposited tungsten emitters for thermionic converters Final report

Performance of deposited tungsten as thermionic converte

CAPS-1 and CAPS-2 are essential synaptic vesicle priming proteins

SummaryBefore transmitter-filled synaptic vesicles can fuse with the plasma membrane upon stimulation they have to be primed to fusion competence. The regulation of this priming process controls the strength and plasticity of synaptic transmission between neurons, which in turn determines many complex brain functions. We show that CAPS-1 and CAPS-2 are essential components of the synaptic vesicle priming machinery. CAPS-deficient neurons contain no or very few fusion competent synaptic vesicles, which causes a selective impairment of fast phasic transmitter release. Increases in the intracellular Ca2+ levels can transiently revert this defect. Our findings demonstrate that CAPS proteins generate and maintain a highly fusion competent synaptic vesicle pool that supports phasic Ca2+ triggered release of transmitters

Automatic, global registration in laparoscopic liver surgery

PURPOSE: The initial registration of a 3D pre-operative CT model to a 2D laparoscopic video image in augmented reality systems for liver surgery needs to be fast, intuitive to perform and with minimal interruptions to the surgical intervention. Several recent methods have focussed on using easily recognisable landmarks across modalities. However, these methods still need manual annotation or manual alignment. We propose a novel, fully automatic pipeline for 3D-2D global registration in laparoscopic liver interventions. METHODS: Firstly, we train a fully convolutional network for the semantic detection of liver contours in laparoscopic images. Secondly, we propose a novel contour-based global registration algorithm to estimate the camera pose without any manual input during surgery. The contours used are the anterior ridge and the silhouette of the liver. RESULTS: We show excellent generalisation of the semantic contour detection on test data from 8 clinical cases. In quantitative experiments, the proposed contour-based registration can successfully estimate a global alignment with as little as 30% of the liver surface, a visibility ratio which is characteristic of laparoscopic interventions. Moreover, the proposed pipeline showed very promising results in clinical data from 5 laparoscopic interventions. CONCLUSIONS: Our proposed automatic global registration could make augmented reality systems more intuitive and usable for surgeons and easier to translate to operating rooms. Yet, as the liver is deformed significantly during surgery, it will be very beneficial to incorporate deformation into our method for more accurate registration

Gesture Recognition in Robotic Surgery with Multimodal Attention

Automatically recognising surgical gestures from surgical data is an important building block of automated activity recognition and analytics, technical skill assessment, intra-operative assistance and eventually robotic automation. The complexity of articulated instrument trajectories and the inherent variability due to surgical style and patient anatomy make analysis and fine-grained segmentation of surgical motion patterns from robot kinematics alone very difficult. Surgical video provides crucial information from the surgical site with context for the kinematic data and the interaction between the instruments and tissue. Yet sensor fusion between the robot data and surgical video stream is non-trivial because the data have different frequency, dimensions and discriminative capability. In this paper, we integrate multimodal attention mechanisms in a two-stream temporal convolutional network to compute relevance scores and weight kinematic and visual feature representations dynamically in time, aiming to aid multimodal network training and achieve effective sensor fusion. We report the results of our system on the JIGSAWS benchmark dataset and on a new in vivo dataset of suturing segments from robotic prostatectomy procedures. Our results are promising and obtain multimodal prediction sequences with higher accuracy and better temporal structure than corresponding unimodal solutions. Visualization of attention scores also gives physically interpretable insights on network understanding of strengths and weaknesses of each sensor

Elastic properties of grafted microtubules

We use single-particle tracking to study the elastic properties of single microtubules grafted to a substrate. Thermal fluctuations of the free microtubule's end are recorded, in order to measure position distribution functions from which we calculate the persistence length of microtubules with contour lengths between 2.6 and 48 micrometers. We find the persistence length to vary by more than a factor of 20 over the total range of contour lengths. Our results support the hypothesis that shearing between protofilaments contributes significantly to the mechanics of microtubules.Comment: 9 pages, 3 figure

Systematics of collective correlation energies from self-consistent mean-field calculations

The collective ground-state correlations stemming from low-lying quadrupole excitations are computed microscopically. To that end, the self-consistent mean-field model is employed on the basis of the Skyrme-Hartre-Fock (SHF) functional augmented by BCS pairing. The microscopic-macroscopic mapping is achieved by quadrupole-constrained mean-field calculations which are processed further in the generator-coordinate method (GCM) at the level of the Gaussian overlap approximation (GOA). We study the correlation effects on energy, charge radii, and surface thickness for a great variety of semi-magic nuclei. A key issue is to work out the influence of variations of the SHF functional. We find that collective ground-state correlations (GSC) are robust under change of nuclear bulk properties (e.g., effective mass, symmetry energy) or of spin-orbit coupling. Some dependence on the pairing strength is observed. This, however, does not change the general conclusion that collective GSC obey a general pattern and that their magnitudes are rather independent of the actual SHF parameters.Comment: 13 pages, 13 figure