18,750 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
NiftyNet: a deep-learning platform for medical imaging
Medical image analysis and computer-assisted intervention problems are
increasingly being addressed with deep-learning-based solutions. Established
deep-learning platforms are flexible but do not provide specific functionality
for medical image analysis and adapting them for this application requires
substantial implementation effort. Thus, there has been substantial duplication
of effort and incompatible infrastructure developed across many research
groups. This work presents the open-source NiftyNet platform for deep learning
in medical imaging. The ambition of NiftyNet is to accelerate and simplify the
development of these solutions, and to provide a common mechanism for
disseminating research outputs for the community to use, adapt and build upon.
NiftyNet provides a modular deep-learning pipeline for a range of medical
imaging applications including segmentation, regression, image generation and
representation learning applications. Components of the NiftyNet pipeline
including data loading, data augmentation, network architectures, loss
functions and evaluation metrics are tailored to, and take advantage of, the
idiosyncracies of medical image analysis and computer-assisted intervention.
NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D
and 3D images and computational graphs by default.
We present 3 illustrative medical image analysis applications built using
NiftyNet: (1) segmentation of multiple abdominal organs from computed
tomography; (2) image regression to predict computed tomography attenuation
maps from brain magnetic resonance images; and (3) generation of simulated
ultrasound images for specified anatomical poses.
NiftyNet enables researchers to rapidly develop and distribute deep learning
solutions for segmentation, regression, image generation and representation
learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge
Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6
figures; Update includes additional applications, updated author list and
formatting for journal submissio
Nonlinear tube-fitting for the analysis of anatomical and functional structures
We are concerned with the estimation of the exterior surface and interior
summaries of tube-shaped anatomical structures. This interest is motivated by
two distinct scientific goals, one dealing with the distribution of HIV
microbicide in the colon and the other with measuring degradation in
white-matter tracts in the brain. Our problem is posed as the estimation of the
support of a distribution in three dimensions from a sample from that
distribution, possibly measured with error. We propose a novel tube-fitting
algorithm to construct such estimators. Further, we conduct a simulation study
to aid in the choice of a key parameter of the algorithm, and we test our
algorithm with validation study tailored to the motivating data sets. Finally,
we apply the tube-fitting algorithm to a colon image produced by single photon
emission computed tomography (SPECT) and to a white-matter tract image produced
using diffusion tensor imaging (DTI).Comment: Published in at http://dx.doi.org/10.1214/10-AOAS384 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Thermal dosimetry for bladder hyperthermia treatment. An overview.
The urinary bladder is a fluid-filled organ. This makes, on the one hand, the internal surface of the bladder wall relatively easy to heat and ensures in most cases a relatively homogeneous temperature distribution; on the other hand the variable volume, organ motion, and moving fluid cause artefacts for most non-invasive thermometry methods, and require additional efforts in planning accurate thermal treatment of bladder cancer. We give an overview of the thermometry methods currently used and investigated for hyperthermia treatments of bladder cancer, and discuss their advantages and disadvantages within the context of the specific disease (muscle-invasive or non-muscle-invasive bladder cancer) and the heating technique used. The role of treatment simulation to determine the thermal dose delivered is also discussed. Generally speaking, invasive measurement methods are more accurate than non-invasive methods, but provide more limited spatial information; therefore, a combination of both is desirable, preferably supplemented by simulations. Current efforts at research and clinical centres continue to improve non-invasive thermometry methods and the reliability of treatment planning and control software. Due to the challenges in measuring temperature across the non-stationary bladder wall and surrounding tissues, more research is needed to increase our knowledge about the penetration depth and typical heating pattern of the various hyperthermia devices, in order to further improve treatments. The ability to better determine the delivered thermal dose will enable clinicians to investigate the optimal treatment parameters, and consequentially, to give better controlled, thus even more reliable and effective, thermal treatments
Current status and new horizons in Monte Carlo simulation of X-ray CT scanners
With the advent of powerful computers and parallel processing including Grid technology, the use of Monte Carlo (MC) techniques for radiation transport simulation has become the most popular method for modeling radiological imaging systems and particularly X-ray computed tomography (CT). The stochastic nature of involved processes such as X-ray photons generation, interaction with matter and detection makes MC the ideal tool for accurate modeling. MC calculations can be used to assess the impact of different physical design parameters on overall scanner performance, clinical image quality and absorbed dose assessment in CT examinations, which can be difficult or even impossible to estimate by experimental measurements and theoretical analysis. Simulations can also be used to develop and assess correction methods and reconstruction algorithms aiming at improving image quality and quantitative procedures. This paper focuses mainly on recent developments and future trends in X-ray CT MC modeling tools and their areas of application. An overview of existing programs and their useful features will be given together with recent developments in the design of computational anthropomorphic models of the human anatomy. It should be noted that due to limited space, the references contained herein are for illustrative purposes and are not inclusive; no implication that those chosen are better than others not mentioned is intende
Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology
Until recently, Computer-Aided Medical Interventions (CAMI) and Medical
Robotics have focused on rigid and non deformable anatomical structures.
Nowadays, special attention is paid to soft tissues, raising complex issues due
to their mobility and deformation. Mini-invasive digestive surgery was probably
one of the first fields where soft tissues were handled through the development
of simulators, tracking of anatomical structures and specific assistance
robots. However, other clinical domains, for instance urology, are concerned.
Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU,
radiofrequency, or cryoablation), increasingly early detection of cancer, and
use of interventional and diagnostic imaging modalities, recently opened new
challenges to the urologist and scientists involved in CAMI. This resulted in
the last five years in a very significant increase of research and developments
of computer-aided urology systems. In this paper, we propose a description of
the main problems related to computer-aided diagnostic and therapy of soft
tissues and give a survey of the different types of assistance offered to the
urologist: robotization, image fusion, surgical navigation. Both research
projects and operational industrial systems are discussed
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