56,041 research outputs found
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Simultaneous Hand Pose and Skeleton Bone-Lengths Estimation from a Single Depth Image
Articulated hand pose estimation is a challenging task for human-computer
interaction. The state-of-the-art hand pose estimation algorithms work only
with one or a few subjects for which they have been calibrated or trained.
Particularly, the hybrid methods based on learning followed by model fitting or
model based deep learning do not explicitly consider varying hand shapes and
sizes. In this work, we introduce a novel hybrid algorithm for estimating the
3D hand pose as well as bone-lengths of the hand skeleton at the same time,
from a single depth image. The proposed CNN architecture learns hand pose
parameters and scale parameters associated with the bone-lengths
simultaneously. Subsequently, a new hybrid forward kinematics layer employs
both parameters to estimate 3D joint positions of the hand. For end-to-end
training, we combine three public datasets NYU, ICVL and MSRA-2015 in one
unified format to achieve large variation in hand shapes and sizes. Among
hybrid methods, our method shows improved accuracy over the state-of-the-art on
the combined dataset and the ICVL dataset that contain multiple subjects. Also,
our algorithm is demonstrated to work well with unseen images.Comment: This paper has been accepted and presented in 3DV-2017 conference
held at Qingdao, China. http://irc.cs.sdu.edu.cn/3dv
SurfelWarp: Efficient Non-Volumetric Single View Dynamic Reconstruction
We contribute a dense SLAM system that takes a live stream of depth images as
input and reconstructs non-rigid deforming scenes in real time, without
templates or prior models. In contrast to existing approaches, we do not
maintain any volumetric data structures, such as truncated signed distance
function (TSDF) fields or deformation fields, which are performance and memory
intensive. Our system works with a flat point (surfel) based representation of
geometry, which can be directly acquired from commodity depth sensors. Standard
graphics pipelines and general purpose GPU (GPGPU) computing are leveraged for
all central operations: i.e., nearest neighbor maintenance, non-rigid
deformation field estimation and fusion of depth measurements. Our pipeline
inherently avoids expensive volumetric operations such as marching cubes,
volumetric fusion and dense deformation field update, leading to significantly
improved performance. Furthermore, the explicit and flexible surfel based
geometry representation enables efficient tackling of topology changes and
tracking failures, which makes our reconstructions consistent with updated
depth observations. Our system allows robots to maintain a scene description
with non-rigidly deformed objects that potentially enables interactions with
dynamic working environments.Comment: RSS 2018. The video and source code are available on
https://sites.google.com/view/surfelwarp/hom
Towards automated visual flexible endoscope navigation
Background:\ud
The design of flexible endoscopes has not changed significantly in the past 50 years. A trend is observed towards a wider application of flexible endoscopes with an increasing role in complex intraluminal therapeutic procedures. The nonintuitive and nonergonomical steering mechanism now forms a barrier in the extension of flexible endoscope applications. Automating the navigation of endoscopes could be a solution for this problem. This paper summarizes the current state of the art in image-based navigation algorithms. The objectives are to find the most promising navigation system(s) to date and to indicate fields for further research.\ud
Methods:\ud
A systematic literature search was performed using three general search terms in two medical–technological literature databases. Papers were included according to the inclusion criteria. A total of 135 papers were analyzed. Ultimately, 26 were included.\ud
Results:\ud
Navigation often is based on visual information, which means steering the endoscope using the images that the endoscope produces. Two main techniques are described: lumen centralization and visual odometry. Although the research results are promising, no successful, commercially available automated flexible endoscopy system exists to date.\ud
Conclusions:\ud
Automated systems that employ conventional flexible endoscopes show the most promising prospects in terms of cost and applicability. To produce such a system, the research focus should lie on finding low-cost mechatronics and technologically robust steering algorithms. Additional functionality and increased efficiency can be obtained through software development. The first priority is to find real-time, robust steering algorithms. These algorithms need to handle bubbles, motion blur, and other image artifacts without disrupting the steering process
Simulation of VUV electroluminescence in micropattern gaseous detectors: the case of GEM and MHSP
Electroluminescence produced during avalanche development in gaseous
avalanche detectors is an useful information for triggering, calorimetry and
tracking in gaseous detectors. Noble gases present high electroluminescence
yields, emitting mainly in the VUV region. The photons can provide signal
readout if appropriate photosensors are used. Micropattern gaseous detectors
are good candidates for signal amplification in high background and/or low rate
experiments due to their high electroluminescence yields and radiopurity. In
this work, the VUV light responses of the Gas Electron Multiplier and of the
Micro-Hole Strip Plate, working with pure xenon, are simulated and studied in
detail using a new and versatile C++ toolkit. It is shown that the solid angle
subtended by a photosensor placed below the microstructures depends on the
operating conditions. The obtained absolute EL yields, determined for different
gas pressures and as functions of the applied voltage, are compared with those
determined experimentally.Comment: Accepted for publication in Journal of Instrumentatio
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