5,026 research outputs found
i3PosNet: Instrument Pose Estimation from X-Ray in temporal bone surgery
Purpose: Accurate estimation of the position and orientation (pose) of
surgical instruments is crucial for delicate minimally invasive temporal bone
surgery. Current techniques lack in accuracy and/or line-of-sight constraints
(conventional tracking systems) or expose the patient to prohibitive ionizing
radiation (intra-operative CT). A possible solution is to capture the
instrument with a c-arm at irregular intervals and recover the pose from the
image.
Methods: i3PosNet infers the position and orientation of instruments from
images using a pose estimation network. Said framework considers localized
patches and outputs pseudo-landmarks. The pose is reconstructed from
pseudo-landmarks by geometric considerations.
Results: We show i3PosNet reaches errors less than 0.05mm. It outperforms
conventional image registration-based approaches reducing average and maximum
errors by at least two thirds. i3PosNet trained on synthetic images generalizes
to real x-rays without any further adaptation.
Conclusion: The translation of Deep Learning based methods to surgical
applications is difficult, because large representative datasets for training
and testing are not available. This work empirically shows sub-millimeter pose
estimation trained solely based on synthetic training data.Comment: Accepted at International journal of computer assisted radiology and
surgery pending publicatio
On Kottler's path: origin and evolution of the premetric program in gravity and in electrodynamics
In 1922, Kottler put forward the program to remove the gravitational
potential, the metric of spacetime, from the fundamental equations in physics
as far as possible. He successfully applied this idea to Newton's
gravitostatics and to Maxwell's electrodynamics, where Kottler recast the field
equations in premetric form and specified a metric-dependent constitutive law.
We will discuss the basics of the premetric approach and some of its beautiful
consequences, like the division of universal constants into two classes. We
show that classical electrodynamics can be developed without a metric quite
straightforwardly: the Maxwell equations, together with a local and linear
response law for electromagnetic media, admit a consistent premetric
formulation. Kottler's program succeeds here without provisos. In Kottler's
approach to gravity, making the theory relativistic, two premetric
quasi-Maxwellian field equations arise, but their field variables, if
interpreted in terms of general relativity, do depend on the metric. However,
one can hope to bring the Kottler idea to work by using the teleparallelism
equivalent of general relativity, where the gravitational potential, the
coframe, can be chosen in a premetric way.Comment: 72 pages latex with 6 figures; based on an invited talk given at the
Annual Meeting of the German Physical Society (DPG) in Berlin on 20 March
2015, Working Group on Philosophy of Physics (AGPhil); a short version will
be submitted to IJMP
PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time
Marker-less 3D human motion capture from a single colour camera has seen
significant progress. However, it is a very challenging and severely ill-posed
problem. In consequence, even the most accurate state-of-the-art approaches
have significant limitations. Purely kinematic formulations on the basis of
individual joints or skeletons, and the frequent frame-wise reconstruction in
state-of-the-art methods greatly limit 3D accuracy and temporal stability
compared to multi-view or marker-based motion capture. Further, captured 3D
poses are often physically incorrect and biomechanically implausible, or
exhibit implausible environment interactions (floor penetration, foot skating,
unnatural body leaning and strong shifting in depth), which is problematic for
any use case in computer graphics. We, therefore, present PhysCap, the first
algorithm for physically plausible, real-time and marker-less human 3D motion
capture with a single colour camera at 25 fps. Our algorithm first captures 3D
human poses purely kinematically. To this end, a CNN infers 2D and 3D joint
positions, and subsequently, an inverse kinematics step finds space-time
coherent joint angles and global 3D pose. Next, these kinematic reconstructions
are used as constraints in a real-time physics-based pose optimiser that
accounts for environment constraints (e.g., collision handling and floor
placement), gravity, and biophysical plausibility of human postures. Our
approach employs a combination of ground reaction force and residual force for
plausible root control, and uses a trained neural network to detect foot
contact events in images. Our method captures physically plausible and
temporally stable global 3D human motion, without physically implausible
postures, floor penetrations or foot skating, from video in real time and in
general scenes. The video is available at
http://gvv.mpi-inf.mpg.de/projects/PhysCapComment: 16 pages, 11 figure
A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance
During spinal fusion surgery, screws are placed close to critical nerves
suggesting the need for highly accurate screw placement. Verifying screw
placement on high-quality tomographic imaging is essential. C-arm Cone-beam CT
(CBCT) provides intraoperative 3D tomographic imaging which would allow for
immediate verification and, if needed, revision. However, the reconstruction
quality attainable with commercial CBCT devices is insufficient, predominantly
due to severe metal artifacts in the presence of pedicle screws. These
artifacts arise from a mismatch between the true physics of image formation and
an idealized model thereof assumed during reconstruction. Prospectively
acquiring views onto anatomy that are least affected by this mismatch can,
therefore, improve reconstruction quality. We propose to adjust the C-arm CBCT
source trajectory during the scan to optimize reconstruction quality with
respect to a certain task, i.e. verification of screw placement. Adjustments
are performed on-the-fly using a convolutional neural network that regresses a
quality index for possible next views given the current x-ray image. Adjusting
the CBCT trajectory to acquire the recommended views results in non-circular
source orbits that avoid poor images, and thus, data inconsistencies. We
demonstrate that convolutional neural networks trained on realistically
simulated data are capable of predicting quality metrics that enable
scene-specific adjustments of the CBCT source trajectory. Using both
realistically simulated data and real CBCT acquisitions of a
semi-anthropomorphic phantom, we show that tomographic reconstructions of the
resulting scene-specific CBCT acquisitions exhibit improved image quality
particularly in terms of metal artifacts. Since the optimization objective is
implicitly encoded in a neural network, the proposed approach overcomes the
need for 3D information at run-time.Comment: 12 page
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