6,619 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
Active MR k-space Sampling with Reinforcement Learning
Deep learning approaches have recently shown great promise in accelerating
magnetic resonance image (MRI) acquisition. The majority of existing work have
focused on designing better reconstruction models given a pre-determined
acquisition trajectory, ignoring the question of trajectory optimization. In
this paper, we focus on learning acquisition trajectories given a fixed image
reconstruction model. We formulate the problem as a sequential decision process
and propose the use of reinforcement learning to solve it. Experiments on a
large scale public MRI dataset of knees show that our proposed models
significantly outperform the state-of-the-art in active MRI acquisition, over a
large range of acceleration factors.Comment: Presented at the 23rd International Conference on Medical Image
Computing and Computer Assisted Intervention, MICCAI 202
Voxlines: Streamline Transparency through Voxelization and View-Dependent Line Orders
As tractography datasets continue to grow in size, there is a need for
improved visualization methods that can capture structural patterns occurring
in large tractography datasets. Transparency is an increasingly important
aspect of finding these patterns in large datasets but is inaccessible to
tractography due to performance limitations. In this paper, we propose a
rendering method that achieves performant rendering of transparent streamlines,
allowing for exploration of deeper brain structures interactively. The method
achieves this through a novel approximate order-independent transparency method
that utilizes voxelization and caching view-dependent line orders per voxel. We
compare our transparency method with existing tractography visualization
software in terms of performance and the ability to capture deeper structures
in the dataset.Comment: 12 pages. 4 figures. Accepted at Computational Diffusion MRI Workshop
(CDMRI) at Medical Image Computing and Computer Assisted Intervention
(MICCAI) 202
Deep convolutional networks for automated detection of posterior-element fractures on spine CT
Injuries of the spine, and its posterior elements in particular, are a common
occurrence in trauma patients, with potentially devastating consequences.
Computer-aided detection (CADe) could assist in the detection and
classification of spine fractures. Furthermore, CAD could help assess the
stability and chronicity of fractures, as well as facilitate research into
optimization of treatment paradigms.
In this work, we apply deep convolutional networks (ConvNets) for the
automated detection of posterior element fractures of the spine. First, the
vertebra bodies of the spine with its posterior elements are segmented in spine
CT using multi-atlas label fusion. Then, edge maps of the posterior elements
are computed. These edge maps serve as candidate regions for predicting a set
of probabilities for fractures along the image edges using ConvNets in a 2.5D
fashion (three orthogonal patches in axial, coronal and sagittal planes). We
explore three different methods for training the ConvNet using 2.5D patches
along the edge maps of 'positive', i.e. fractured posterior-elements and
'negative', i.e. non-fractured elements.
An experienced radiologist retrospectively marked the location of 55
displaced posterior-element fractures in 18 trauma patients. We randomly split
the data into training and testing cases. In testing, we achieve an
area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at
5 or 10 false-positives per patient, respectively. Analysis of our set of
trauma patients demonstrates the feasibility of detecting posterior-element
fractures in spine CT images using computer vision techniques such as deep
convolutional networks.Comment: To be presented at SPIE Medical Imaging, 2016, San Dieg
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