8,140 research outputs found
Label-driven weakly-supervised learning for multimodal deformable image registration
Spatially aligning medical images from different modalities remains a
challenging task, especially for intraoperative applications that require fast
and robust algorithms. We propose a weakly-supervised, label-driven formulation
for learning 3D voxel correspondence from higher-level label correspondence,
thereby bypassing classical intensity-based image similarity measures. During
training, a convolutional neural network is optimised by outputting a dense
displacement field (DDF) that warps a set of available anatomical labels from
the moving image to match their corresponding counterparts in the fixed image.
These label pairs, including solid organs, ducts, vessels, point landmarks and
other ad hoc structures, are only required at training time and can be
spatially aligned by minimising a cross-entropy function of the warped moving
label and the fixed label. During inference, the trained network takes a new
image pair to predict an optimal DDF, resulting in a fully-automatic,
label-free, real-time and deformable registration. For interventional
applications where large global transformation prevails, we also propose a
neural network architecture to jointly optimise the global- and local
displacements. Experiment results are presented based on cross-validating
registrations of 111 pairs of T2-weighted magnetic resonance images and 3D
transrectal ultrasound images from prostate cancer patients with a total of
over 4000 anatomical labels, yielding a median target registration error of 4.2
mm on landmark centroids and a median Dice of 0.88 on prostate glands.Comment: Accepted to ISBI 201
Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review
Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519
Adversarial Deformation Regularization for Training Image Registration Neural Networks
We describe an adversarial learning approach to constrain convolutional
neural network training for image registration, replacing heuristic smoothness
measures of displacement fields often used in these tasks. Using
minimally-invasive prostate cancer intervention as an example application, we
demonstrate the feasibility of utilizing biomechanical simulations to
regularize a weakly-supervised anatomical-label-driven registration network for
aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural
transrectal ultrasound (TRUS) images. A discriminator network is optimized to
distinguish the registration-predicted displacement fields from the motion data
simulated by finite element analysis. During training, the registration network
simultaneously aims to maximize similarity between anatomical labels that
drives image alignment and to minimize an adversarial generator loss that
measures divergence between the predicted- and simulated deformation. The
end-to-end trained network enables efficient and fully-automated registration
that only requires an MR and TRUS image pair as input, without anatomical
labels or simulated data during inference. 108 pairs of labelled MR and TRUS
images from 76 prostate cancer patients and 71,500 nonlinear finite-element
simulations from 143 different patients were used for this study. We show that,
with only gland segmentation as training labels, the proposed method can help
predict physically plausible deformation without any other smoothness penalty.
Based on cross-validation experiments using 834 pairs of independent validation
landmarks, the proposed adversarial-regularized registration achieved a target
registration error of 6.3 mm that is significantly lower than those from
several other regularization methods.Comment: Accepted to MICCAI 201
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
Whole slide image registration for the study of tumor heterogeneity
Consecutive thin sections of tissue samples make it possible to study local
variation in e.g. protein expression and tumor heterogeneity by staining for a
new protein in each section. In order to compare and correlate patterns of
different proteins, the images have to be registered with high accuracy. The
problem we want to solve is registration of gigapixel whole slide images (WSI).
This presents 3 challenges: (i) Images are very large; (ii) Thin sections
result in artifacts that make global affine registration prone to very large
local errors; (iii) Local affine registration is required to preserve correct
tissue morphology (local size, shape and texture). In our approach we compare
WSI registration based on automatic and manual feature selection on either the
full image or natural sub-regions (as opposed to square tiles). Working with
natural sub-regions, in an interactive tool makes it possible to exclude
regions containing scientifically irrelevant information. We also present a new
way to visualize local registration quality by a Registration Confidence Map
(RCM). With this method, intra-tumor heterogeneity and charateristics of the
tumor microenvironment can be observed and quantified.Comment: MICCAI2018 - Computational Pathology and Ophthalmic Medical Image
Analysis - COMPA
Fully Automatic Expression-Invariant Face Correspondence
We consider the problem of computing accurate point-to-point correspondences
among a set of human face scans with varying expressions. Our fully automatic
approach does not require any manually placed markers on the scan. Instead, the
approach learns the locations of a set of landmarks present in a database and
uses this knowledge to automatically predict the locations of these landmarks
on a newly available scan. The predicted landmarks are then used to compute
point-to-point correspondences between a template model and the newly available
scan. To accurately fit the expression of the template to the expression of the
scan, we use as template a blendshape model. Our algorithm was tested on a
database of human faces of different ethnic groups with strongly varying
expressions. Experimental results show that the obtained point-to-point
correspondence is both highly accurate and consistent for most of the tested 3D
face models
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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