328 research outputs found
Deformable Multisurface Segmentation of the Spine for Orthopedic Surgery Planning and Simulation
Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data.
Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user assistance is allowed to disable the prior shape influence during deformation.
Results: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit.
Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation
Multimodal Partial-Volume Correction: Application to 18F-Fluoride PET/CT Bone Metastases Studies
18F-fluoride PET/CT offers the opportunity for accurate skeletal metastasis staging, compared with conventional imaging methods. 18F-fluoride is a bone-specific tracer whose uptake depends on osteoblastic activity. Because of the resulting increase in bone mineralization and sclerosis, the osteoblastic process can also be detected morphologically in CT images. Although CT is characterized by high resolution, the potential of PET is limited by its lower spatial resolution and the resulting partial-volume effect. In this context, the synergy between PET and CT presents an opportunity to resolve this limitation using a novel multimodal approach called synergistic functional–structural resolution recovery (SFS-RR). Its performance is benchmarked against current resolution recovery technology using the point-spread function (PSF) of the scanner in the reconstruction procedure. Methods: The SFS-RR technique takes advantage of the multiresolution property of the wavelet transform applied to both functional and structural images to create a high-resolution PET image that exploits the structural information of CT. Although the method was originally conceived for PET/MR imaging of brain data, an ad hoc version for whole-body PET/CT is proposed here. Three phantom experiments and 2 datasets of metastatic bone 18F-fluoride PET/CT images from primary prostate and breast cancer were used to test the algorithm performances. The SFS-RR images were compared with the manufacturer’s PSF-based reconstruction using the standardized uptake value (SUV) and the metabolic volume as metrics for quantification. Results: When compared with standard PET images, the phantom experiments showed a bias reduction of 14% in activity and 1.3 cm3 in volume estimates for PSF images and up to 20% and 2.5 cm3 for the SFS-RR images. The SFS-RR images were characterized by a higher recovery coefficient (up to 60%) whereas noise levels remained comparable to those of standard PET. The clinical data showed an increase in the SUV estimates for SFS-RR images up to 34% for peak SUV and 50% for maximum SUV and mean SUV. Images were also characterized by sharper lesion contours and better lesion detectability. Conclusion: The proposed methodology generates PET images with improved quantitative and qualitative properties. Compared with standard methods, SFS-RR provides superior lesion segmentation and quantification, which may result in more accurate tumor characterization
Hybrid Speckle Noise Reduction Method for Abdominal Circumference Segmentation of Fetal Ultrasound Images
Fetal biometric size such as abdominal circumference (AC) is used to predict fetal weight or gestational age in ultrasound images. The automatic biometric measurement can improve efficiency in the ultrasonography examination workflow. The unclear boundaries of the abdomen image and the speckle noise presence are the challenges for the automated AC measurement techniques. The main problem to improve the accuracy of the automatic AC segmentation is how to remove noise while retaining the boundary features of objects. In this paper, we proposed a hybrid ultrasound image denoising framework which was a combination of spatial-based filtering method and multiresolution based method. In this technique, an ultrasound image was decomposed into subbands using wavelet transform. A thresholding technique and the anisotropic diffusion method were applied to the detail subbands, at the same time the bilateral filtering modified the approximation subband. The proposed denoising approach had the best performance in the edge preservation level and could improve the accuracy of the abdominal circumference segmentation
Automatic Semantic Segmentation of the Lumbar Spine: Clinical Applicability in a Multi-parametric and Multi-centre Study on Magnetic Resonance Images
One of the major difficulties in medical image segmentation is the high
variability of these images, which is caused by their origin (multi-centre),
the acquisition protocols (multi-parametric), as well as the variability of
human anatomy, the severity of the illness, the effect of age and gender, among
others. The problem addressed in this work is the automatic semantic
segmentation of lumbar spine Magnetic Resonance images using convolutional
neural networks. The purpose is to assign a class label to each pixel of an
image. Classes were defined by radiologists and correspond to different
structural elements like vertebrae, intervertebral discs, nerves, blood
vessels, and other tissues. The proposed network topologies are variants of the
U-Net architecture. Several complementary blocks were used to define the
variants: Three types of convolutional blocks, spatial attention models, deep
supervision and multilevel feature extractor. This document describes the
topologies and analyses the results of the neural network designs that obtained
the most accurate segmentations. Several of the proposed designs outperform the
standard U-Net used as baseline, especially when used in ensembles where the
output of multiple neural networks is combined according to different
strategies.Comment: 19 pages, 9 Figures, 8 Tables; Supplementary Material: 6 pages, 8
Table
An Optimized Spline-Based Registration of a 3D CT to a Set of C-Arm Images
We have developed an algorithm for the rigid-body registration of
a CT volume to a set of C-arm images.
The algorithm uses a gradient-based iterative minimization of a least-squares measure
of dissimilarity between the C-arm images and projections of the
CT volume. To compute projections, we use a novel method for fast
integration of the volume along rays. To improve robustness and
speed, we take advantage of a coarse-to-fine processing of the
volume/image pyramids. To compute the projections of the volume,
the gradient of the dissimilarity measure, and the multiresolution
data pyramids, we use a continuous image/volume model based on
cubic B-splines, which ensures a high interpolation accuracy and a
gradient of the dissimilarity measure that is well defined
everywhere. We show the performance of our algorithm on a human
spine phantom, where the true alignment is determined using a set
of fiducial markers
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
The medical image analysis field has traditionally been focused on the
development of organ-, and disease-specific methods. Recently, the interest in
the development of more 20 comprehensive computational anatomical models has
grown, leading to the creation of multi-organ models. Multi-organ approaches,
unlike traditional organ-specific strategies, incorporate inter-organ relations
into the model, thus leading to a more accurate representation of the complex
human anatomy. Inter-organ relations are not only spatial, but also functional
and physiological. Over the years, the strategies 25 proposed to efficiently
model multi-organ structures have evolved from the simple global modeling, to
more sophisticated approaches such as sequential, hierarchical, or machine
learning-based models. In this paper, we present a review of the state of the
art on multi-organ analysis and associated computation anatomy methodology. The
manuscript follows a methodology-based classification of the different
techniques 30 available for the analysis of multi-organs and multi-anatomical
structures, from techniques using point distribution models to the most recent
deep learning-based approaches. With more than 300 papers included in this
review, we reflect on the trends and challenges of the field of computational
anatomy, the particularities of each anatomical region, and the potential of
multi-organ analysis to increase the impact of 35 medical imaging applications
on the future of healthcare.Comment: Paper under revie
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