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Atlas-Based Whole-Body PET-CT Segmentation Using a Passive Contour Distance
Abstract
In positron emission tomography (PET) imaging, the segmentation of organs is necessary for many quantitative image analysis tasks, e.g., estimation of individual organ concentration or partial volume correction. To this end we present a fully automated approach for wholebody segmentation which enables large-scale and reproducible studies. The approach is based on joint segmentation and atlas registration. The classical active contour approach by Chan and Vese is modified to a novel passive contour energy term with implicitly incorporated information about shape and location of the organs. This new energy is added to a registration functional which is based on both functional (PET) and morphological (CT) data. The proposed method is applied to medical data, given by 13 PET-CT data sets of mice, and quantitatively compared to manually drawn VOIs. An average Dice coefficient of 0.73 ± 0.10 for the left ventricle, 0.88 ± 0.05 for the bladder, and 0.76 ± 0.07 for the kidneys shows the high accuracy of our method.
In the introduction of this book we have seen that PET image acquisition is susceptible to motion artifacts. The first step to overcome this problem is the separation of the measured data into different motion states via gating as described in Sect.?1.3. The next step is the estimation of motion between these gated reconstructions. This step is crucial as its accuracy highly influences the quality of the final motion corrected image.
In this paper we present a fully automated atlas-based whole-body segmentation approach using a registration functional with joint segmentation based on our previous work. The passive contour distance is mathematically remodeled. The approach is validated on mouse data sets.
Many computer vision algorithms have been successfully adapted and applied to biomedical imaging applications. However, biomedical computer vision is far beyond being only an application field. Indeed, it is a wide field with huge potential for developing novel concepts and algorithms and can be seen as a driving force for computer vision research. To emphasize this view of biomedical computer vision we consider a variety of important topics of biomedical imaging in this paper and exemplarily discuss some challenges, the related concepts, techniques, and algorithms.
Respiratory and cardiac motion leads to image degradation in positron emission tomography (PET) studies of the human heart. In this paper we present a novel approach to motion correction based on dual gating and mass-preserving hyperelastic image registration. Thereby, we account for intensity modulations caused by the highly nonrigid cardiac motion. This leads to accurate and realistic motion estimates which are quantitatively validated on software phantom data and carried over to clinically relevant data using a hardware phantom. For patient data, the proposed method is first evaluated in a high statistic (20 min scans) dual gating study of 21 patients. It is shown that the proposed approach properly corrects PET images for dual-cardiac as well as respiratory-motion. In a second study the list mode data of the same patients is cropped to a scan time reasonable for clinical practice (3 min). This low statistic study not only shows the clinical applicability of our method but also demonstrates its robustness against noise obtained by hyperelastic regularization.
In vivo MicroCT imaging of disease models at multiple time points is of great importance for preclinical oncological research, to monitor disease progression. However, the great postural variability between animals in the imaging device complicates data comparison.
In this paper we propose a method for automated registration of whole-body MicroCT follow-up datasets of mice. First, we register the skeleton, the lungs and the skin of an articulated animal atlas (Segars et al. 2004) to MicroCT datasets, yielding point correspondence of these structures over all time points. This correspondence is then used to regularize an intensity-based B-spline registration. This two step approach combines the robustness of model-based registration with the high accuracy of intensity-based registration.
We demonstrate our approach using challenging whole-body in vivo follow-up MicroCT data and obtain subvoxel accuracy for the skeleton and the skin, based on the Euclidean surface distance. The method is computationally efficient and enables high resolution whole-body registration in ≈17 minutes with unoptimized code, mostly executed single-threaded.
Image segmentation and registration have been the two major areas of research in the medical imaging community for decades and still are. In the context of radiation oncology, segmentation and registration methods are widely used for target structure definition such as prostate or head and neck lymph node areas. In the past two years, 45% of all articles published in the most important medical imaging journals and conferences have presented either segmentation or registration methods. In the literature, both categories are treated rather separately even though they have much in common. Registration techniques are used to solve segmentation tasks (e.g. atlas based methods) and vice versa (e.g. segmentation of structures used in a landmark based registration). This article reviews the literature on image segmentation methods by introducing a novel taxonomy based on the amount of shape knowledge being incorporated in the segmentation process. Based on that, we argue that all global shape prior segmentation methods are identical to image registration methods and that such methods thus cannot be characterized as either image segmentation or registration methods. Therefore we propose a new class of methods that are able solve both segmentation and registration tasks. We call it regmentation. Quantified on a survey of the current state of the art medical imaging literature, it turns out that 25% of the methods are pure registration methods, 46% are pure segmentation methods and 29% are regmentation methods. The new view on image segmentation and registration provides a consistent taxonomy in this context and emphasizes the importance of regmentation in current medical image processing research and radiation oncology image-guided applications.
We present a flexible image reconstruction framework for emission tomography data called EMRECON. The software includes multiple expectation maximization based reconstruction algorithms as well as support for several scanner geometries. In order to implement novel reconstruction techniques (e.g. TV-based regularization or combined reconstruction and motion correction) or scanner models, full access to every stage of the reconstruction pipeline is vital. EMrecon is fully open and well-documented, thus permits testing without the need to care about data formats or standard reconstruction and data correction algorithms. Due to the GATE-like syntax new scanner geometries, including an exact definition of each single crystal, can be added easily. The parallel (multi-core) C implementation was successfully tested on several Linux distributions. This makes EMRECON a useful tool for the development of new reconstruction algorithms and also serves as a platform for testing different scanner geometries.
This paper is devoted to the analysis and the extraction of information from bio-medical images. The proposed technique is
based on object and contour detection, curve evolution and segmentation. We present a particular active contour model for
2D and 3D images, formulated using the level set method, and based on a 2-phase piecewise-constant segmentation. We then show
how this model can be generalized to segmentation of images with more than two segments. The techniques used are based on
the Mumford-Shah [21] model. By the proposed models, we can extract in addition measurements of the detected objects, such as average intensity,
perimeter, area, or volume. Such informations are useful when in particular a time evolution of the subject is known, or when
we need to make comparisons between different subjects, for instance between a normal subject and an abnormal one. Finally,
all these will give more informations about the dynamic of a disease, or about how the human body growths. We illustrate the
efficiency of the proposed models by calculations on two-dimensional and three-dimensional bio-medical images.
This book really shows how registration works: the flip-book appearing at the top right corners shows a registration of a human knee from bent to straight position (keeping bones rigid). Of course, the book also provides insight into concepts and practical tools. The presented framework exploits techniques from various fields such as image processing, numerical linear algebra, and optimization. Therefore, a brief overview of some preliminary literature in those fields is presented in the introduction (references [1–51]), and registration-specific literature is assembled at the end of the book (references [52–212]). Examples and results are based on the FAIR software, a package written in MATLAB. The FAIR software, as well as a PDF version of this entire book, can be freely downloaded from www.siam.org/books/fa06.
This book would not have been possible without the help of Bernd Fischer, Eldad Haber, Claudia Kremling, Jim Nagy, and the Safir Research Group from Lübeck: Sven Barendt, Björn Beuthin, Konstantin Ens, Stefan Heldmann, Sven Kabus, Janine Olesch, Nils Papenberg, Hanno Schumacher, and Stefan Wirtz. I'm also indebted to Sahar Alipour, Reza Heydarian, Raya Horesh, Ramin Mafi, and Bahram Marami Dizaji for improving the manuscript.
In vivo imaging of MMPs is of great (pre)clinical interest and can potentially be realized with modern three-dimensional and noninvasive in vivo molecular imaging techniques such as positron emission tomography (PET). Consequently, MMP inhibitors (MMPIs) radiolabeled with positron emitting nuclides (e.g., (18)F) represent a suitable tool for the visualization of activated MMPs with PET. On the basis of our previous work and results regarding radiolabeled and unlabeled derivatives of the nonselective MMPIs, we discovered a new class of fluorinated MMPIs with a triazole-substituted hydroxamate substructure. These novel MMPIs are characterized by an increased hydrophilicity compared with the lead structures and excellent MMP inhibition potencies for MMP-2, MMP-8, MMP-9, and MMP-13 (IC(50) = 0.006-107 nM). Therefore, one promising fluorinated triazole-substituted hydroxamate (30b) was selected and resynthesised as its (18)F-labeled version to yield the potential PET radioligand [(18)F]30b. The biodistribution behavior of this novel compound was investigated with small animal PET.
Traditionally, segmentation and registration have been solved as two independent problems, even though it is often the case that the solution to one impacts the solution to the other. In this paper, we introduce a geometric, variational framework that uses active contours to simultaneously segment and register features from multiple images. The key observation is that multiple images may be segmented by evolving a single contour as well as the mappings of that contour into each image.
Magnetic resonance imaging (MRI)-guided partial volume effect correction (PVC) in brain positron emission tomography (PET) is now a well-established approach to compensate the large bias in the estimate of regional radioactivity concentration, especially for small structures. The accuracy of the algorithms developed so far is, however, largely dependent on the performance of segmentation methods partitioning MRI brain data into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of three brain MRI segmentation algorithms using simulated and clinical brain MR data was performed, and subsequently their impact on PVC in 18F-FDG and 18F-DOPA brain PET imaging was assessed. Two algorithms, the first is bundled in the Statistical Parametric Mapping (SPM2) package while the other is the Expectation Maximization Segmentation (EMS) algorithm, incorporate a priori probability images derived from MR images of a large number of subjects. The third, here referred to as the HBSA algorithm, is a histogram-based segmentation algorithm incorporating an Expectation Maximization approach to model a four-Gaussian mixture for both global and local histograms. Simulated under different combinations of noise and intensity non-uniformity, MR brain phantoms with known true volumes for the different brain classes were generated. The algorithms' performance was checked by calculating the kappa index assessing similarities with the "ground truth" as well as multiclass type I and type II errors including misclassification rates. The impact of image segmentation algorithms on PVC was then quantified using clinical data. The segmented tissues of patients' brain MRI were given as input to the region of interest (RoI)-based geometric transfer matrix (GTM) PVC algorithm, and quantitative comparisons were made. The results of digital MRI phantom studies suggest that the use of HBSA produces the best performance for WM classification. For GM classification, it is suggested to use the EMS. Segmentation performed on clinical MRI data show quite substantial differences, especially when lesions are present. For the particular case of PVC, SPM2 and EMS algorithms show very similar results and may be used interchangeably. The use of HBSA is not recommended for PVC. The partial volume corrected activities in some regions of the brain show quite large relative differences when performing paired analysis on 2 algorithms, implying a careful choice of the segmentation algorithm for GTM-based PVC.
We have constructed a three-dimensional (3D) whole body mouse atlas from coregistered x-ray CT and cryosection data of a normal nude male mouse. High quality PET, x-ray CT and cryosection images were acquired post mortem from a single mouse placed in a stereotactic frame with fiducial markers visible in all three modalities. The image data were coregistered to a common coordinate system using the fiducials and resampled to an isotropic 0.1 mm voxel size. Using interactive editing tools we segmented and labelled whole brain, cerebrum, cerebellum, olfactory bulbs, striatum, medulla, masseter muscles, eyes, lachrymal glands, heart, lungs, liver, stomach, spleen, pancreas, adrenal glands, kidneys, testes, bladder, skeleton and skin surface. The final atlas consists of the 3D volume, in which the voxels are labelled to define the anatomical structures listed above, with coregistered PET, x-ray CT and cryosection images. To illustrate use of the atlas we include simulations of 3D bioluminescence and PET image reconstruction. Optical scatter and absorption values are assigned to each organ to simulate realistic photon transport within the animal for bioluminescence imaging. Similarly, 511 keV photon attenuation values are assigned to each structure in the atlas to simulate realistic photon attenuation in PET. The Digimouse atlas and data are available at http://neuroimage.usc.edu/Digimouse.html.
We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We give a numerical algorithm using finite differences. Finally, we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.
We propose a new multiphase level set framework for image segmentation using the Mumford and Shah model, for piecewise constant and piecewise smooth optimal approximations. The proposed method is also a generalization of an active contour model without edges based 2-phase segmentation, developed by the authors earlier in (Chan and Vese 1999), (Chan and Vese 2001). The multiphase level set formulation is new and of interest on its own: by construction, it automatically avoids the problems of vacuum and overlap; it needs only log n level set functions for n phases in the piecewise constant case; it can represent boundaries with complex topologies, including triple junctions; in the piecewise smooth case, only two level set functions formally suce to represent any partition, based on The Four-Color Theorem. Finally, we validate the proposed models by numerical results for signal and image denoising and segmentation, implemented using the Osher and Sethian level set method.