Defining myocardial contours is often the most time consuming portion of dynamic cardiac MRI image analysis. Displacement encoding with stimulated echoes (DENSE) is a quantitative MRI technique that encodes tissue displacement into the phase of the complex MRI images. Cine DENSE provides a time series of these images, thus facilitating the non-invasive study of myocardial kinematics. Epicardial and endocardial contours need to be defined at each frame on cine DENSE images for the quantification of regional displacement and strain as a function of time. This work presents a reliable and effective two dimensional semi-automated segmentation technique that uses the encoded motion to project a manually defined region of interest through time. Contours can then easily be extracted for each cardiac phase. This method boasts several advantages, including, 1. parameters are based on practical physiological limits, 2. contours are calculated for the first few cardiac phases, where it is difficult to visually distinguish blood from myocardium, and 3. the method is independent of the shape of the tissue delineated and can be applied to short- or long-axis views, and on arbitrary regions of interest. Motion-guided contours were compared to manual contours for six conventional and six slice-followed mid-ventricular short-axis cine DENSE datasets. Using an area measure of segmentation error, the accuracy of the segmentation algorithm was shown to be similar to inter-observer variability. In addition, a radial segmentation error metric was introduced for short-axis data. The average radial epicardial segmentation error was 0.36±0.08 and 0.40±0.10 pixels for slice followed and conventional cine DENSE, respectively, and the average radial endocardial segmentation error was 0.46±0.12 and 0.46±0.16 pixels for slice following and conventional cine DENSE, respectively. Motion-guided segmentation employs the displacement-encoded phase shifts intrinsic to DENSE MRI to accurately propagate a single set of pre-defined contours throughout the remaining cardiac phases.
Cardiac MRI; DENSE; myocardial tagging; segmentation; tissue tracking
We are developing a computer-aided diagnosis (CAD) system to classify malignant and benign lung nodules found on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a three-dimensional (3D) active contour (AC) method. A data set of 96 lung nodules (44 malignant, 52 benign) from 58 patients was used in this study. The 3D AC model is based on two-dimensional AC with the addition of three new energy components to take advantage of 3D information: (1) 3D gradient, which guides the active contour to seek the object surface, (2) 3D curvature, which imposes a smoothness constraint in the z direction, and (3) mask energy, which penalizes contours that grow beyond the pleura or thoracic wall. The search for the best energy weights in the 3D AC model was guided by a simplex optimization method. Morphological and gray-level features were extracted from the segmented nodule. The rubber band straightening transform (RBST) was applied to the shell of voxels surrounding the nodule. Texture features based on run-length statistics were extracted from the RBST image. A linear discriminant analysis classifier with stepwise feature selection was designed using a second simplex optimization to select the most effective features. Leave-one-case-out resampling was used to train and test the CAD system. The system achieved a test area under the receiver operating characteristic curve (Az) of 0.83±0.04. Our preliminary results indicate that use of the 3D AC model and the 3D texture features surrounding the nodule is a promising approach to the segmentation and classification of lung nodules with CAD. The segmentation performance of the 3D AC model trained with our data set was evaluated with 23 nodules available in the Lung Image Database Consortium (LIDC). The lung nodule volumes segmented by the 3D AC model for best classification were generally larger than those outlined by the LIDC radiologists using visual judgment of nodule boundaries.
computer-aided diagnosis; active contour model; object segmentation; classification; texture analysis; computed tomography (CT); malignancy; pulmonary nodule
Accurate segmentation of neonatal brain MR images remains challenging mainly due to their poor spatial resolution, inverted contrast between white matter and gray matter, and high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although active contour/surface models with geometric information constraint have been successfully applied to adult brain segmentation, they are not fully explored in the neonatal image segmentation. In this paper, we propose a novel neonatal image segmentation method by combining local intensity information, atlas spatial prior, and cortical thickness constraint in a single level-set framework. Besides, we also provide a robust and reliable tissue surface initialization for the proposed method by using a convex optimization technique. Thus, tissue segmentation, as well as inner and outer cortical surface reconstruction, can be obtained simultaneously. The proposed method has been tested on a large neonatal dataset, and the validation on 10 neonatal brain images (with manual segmentations) shows very promising results.
Neonatal tissue segmentation; atlas-based segmentation; convex optimization; coupled level sets
Spinal fusion is a widely and successfully performed strategy for the treatment of spinal deformities and degenerative diseases. The general approach has been to stabilize the spine with implants so that a solid bony fusion between the vertebrae can develop. However, new implant designs have emerged that aim at preservation or restoration of the motion of the spinal segment. In addition to static, load sharing principles, these designs also require a profound knowledge of kinematic and dynamic properties to properly characterise the in vivo performance of the implants.
To address this, an apparatus was developed that enables the intraoperative determination of the load–displacement behavior of spinal motion segments. The apparatus consists of a sensor-equipped distractor to measure the applied force between the transverse processes, and an optoelectronic camera to track the motion of vertebrae and the distractor. In this intraoperative trial, measurements from two patients with adolescent idiopathic scoliosis with right thoracic curves were made at four motion segments each.
At a lateral bending moment of 5 N m, the mean flexibility of all eight motion segments was 0.18 ± 0.08°/N m on the convex side and 0.24 ± 0.11°/N m on the concave side.
The results agree with published data obtained from cadaver studies with and without axial preload. Intraoperatively acquired data with this method may serve as an input for mathematical models and contribute to the development of new implants and treatment strategies.
Scoliosis; Motion segment; Spine; Mechanical properties; In vivo measurements
The noninvasive assessment of cardiac function is of first
importance for the diagnosis of cardiovascular diseases. Among all medical scanners only a few enables radiologists to evaluate the local cardiac motion. Tagged cardiac MRI is one of them. This protocol generates on Short-Axis (SA) sequences a dark grid which is deformed in accordance
with the cardiac motion. Tracking the grid allows specialists a local estimation of cardiac geometrical parameters within myocardium. The work described in this paper aims to automate the myocardial contours detection in order to optimize the detection and the tracking of the grid of tags within myocardium. The method we have developed for endocardial
and epicardial contours detection is based on the use of texture analysis
and active contours models. Texture analysis allows us to define energy
maps more efficient than those usually used in active contours methods
where attractor is often based on gradient and which were useless in our
case of study, for quality of tagged cardiac MRI is very poor.
The extraction of brain tissue from magnetic resonance head images, is an important image processing step for the analyses of neuroimage data. The authors have developed an automated and simple brain extraction method using an improved geometric active contour model.
The method uses an improved geometric active contour model which can not only solve the boundary leakage problem but also is less sensitive to intensity inhomogeneity. The method defines the initial function as a binary level set function to improve computational efficiency. The method is applied to both our data and Internet brain MR data provided by the Internet Brain Segmentation Repository.
The results obtained from our method are compared with manual segmentation results using multiple indices. In addition, the method is compared to two popular methods, Brain extraction tool and Model-based Level Set.
The proposed method can provide automated and accurate brain extraction result with high efficiency.
We present a novel 3-D deformable model-based approach for accurate, robust, and automated tissue segmentation of brain MRI data of single as well as multiple magnetic resonance sequences. The main contribution of this study is that we employ an edge-based geodesic active contour for the segmentation task by integrating both image edge geometry and voxel statistical homogeneity into a novel hybrid geometric–statistical feature to regularize contour convergence and extract complex anatomical structures. We validate the accuracy of the segmentation results on simulated brain MRI scans of both single T1-weighted and multiple T1/T2/PD-weighted sequences. We also demonstrate the robustness of the proposed method when applied to clinical brain MRI scans. When compared to a current state-of-the-art region-based level-set segmentation formulation, our white matter and gray matter segmentation resulted in significantly higher accuracy levels with a mean improvement in Dice similarity indexes of 8.55% (p < 0.0001) and 10.18% (p < 0.0001), respectively.
3-D image segmentation; brain segmentation; deformable models; geodesic active contour
The correct segmentation of blood vessels in optical coherence tomography (OCT) images may be an important requirement for the analysis of intra-retinal layer thickness in human retinal diseases. We developed a shape model based procedure for the automatic segmentation of retinal blood vessels in spectral domain (SD)-OCT scans acquired with the Spectralis OCT system. The segmentation procedure is based on a statistical shape model that has been created through manual segmentation of vessels in a training phase. The actual segmentation procedure is performed after the approximate vessel position has been defined by a shadowgraph that assigns the lateral vessel positions. The active shape model method is subsequently used to segment blood vessel contours in axial direction. The automated segmentation results were validated against the manual segmentation of the same vessels by three expert readers. Manual and automated segmentations of 168 blood vessels from 34 B-scans were analyzed with respect to the deviations in the mean Euclidean distance and surface area. The mean Euclidean distance between the automatically and manually segmented contours (on average 4.0 pixels respectively 20 µm against all three experts) was within the range of the manually marked contours among the three readers (approximately 3.8 pixels respectively 18 µm for all experts). The area deviations between the automated and manual segmentation also lie within the range of the area deviations among the 3 clinical experts. Intra reader variability for the experts was between 0.9 and 0.94. We conclude that the automated segmentation approach is able to segment blood vessels with comparable accuracy as expert readers and will provide a useful tool in vessel analysis of whole C-scans, and in particular in multicenter trials.
(170.4500) Optical coherence tomography; (110.6880) Three-dimensional image acquisition; (100.0100) Image processing; (100.3008) Image recognition, algorithms and filters
We propose an active mask segmentation framework that combines the advantages of statistical modeling, smoothing, speed and flexibility offered by the traditional methods of region-growing, multiscale, multiresolution and active contours respectively. At the crux of this framework is a paradigm shift from evolving contours in the continuous domain to evolving multiple masks in the discrete domain. Thus, the active mask framework is particularly suited to segment digital images. We demonstrate the use of the framework in practice through the segmentation of punctate patterns in fluorescence microscope images. Experiments reveal that statistical modeling helps the multiple masks converge from a random initial configuration to a meaningful one. This obviates the need for an involved initialization procedure germane to most of the traditional methods used to segment fluorescence microscope images. While we provide the mathematical details of the functions used to segment fluorescence microscope images, this is only an instantiation of the active mask framework. We suggest some other instantiations of the framework to segment different types of images.
active contours; active masks; cellular automata; fluorescence microscopy; multiresolution; multiscale; segmentation
Purpose. To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed) and target (reference image). Point landmarks are placed at regular intervals on contours of anatomical features, and their positions are optimized along the contour surface by a function composed of curvature similarity and displacements of the homologous landmarks. The method was evaluated in two cases (n = 5 each). In one, MRI was registered to histological sections; in the second, geometric distortions in EPI MRI were corrected. Normalized mutual information and target registration error were calculated to compare the registration accuracy of the automatically and manually generated landmarks. Results. Statistical analyses demonstrated significant improvement (P < 0.05) in registration accuracy by landmark optimization in most data sets and trends towards improvement (P < 0.1) in others as compared to manual landmark selection.
The purpose of this study was to investigate in vivo three- dimensional tibiofemoral kinematics and femoral condylar motion in knees with anterior cruciate ligament (ACL) deficiency during a knee bend activity. Ten patients with unilateral ACL rupture were enrolled. Both the injured and contralateral normal knees were imaged using biplane radiography at extension and at 15°, 30°, 60°, 90°, and 120° of flexion. Bilateral knees were next scanned by computed tomography, from which bilateral three-dimensional knee models were created. The in vivo tibiofemoral motion at each flexion position was reproduced through image registration using the knee models and biplane radiographs. A joint coordinate system containing the geometric center axis of the femur was used to measure the tibiofemoral motion. In ACL deficiency, the lateral femoral condyle was located significantly more posteriorly at extension and at 15° (p < 0.05), whereas the medial condylar position was changed only slightly. This constituted greater posterior translation and external rotation of the femur relative to the tibia at extension and at 15° (p < 0.05). Furthermore, ACL deficiency led to a significantly reduced extent of posterior movement of the lateral condyle during flexion from 15° to 60° (p < 0.05). Coupled with an insignificant change in the motion of the medial condyle, the femur moved less posteriorly with reduced extent of external rotation during flexion from 15° to 60° in ACL deficiency (p < 0.05). The medial- lateral and proximal-distal translations of the medial and lateral condyles and the femoral adduction-abduction rotation were insignificantly changed after ACL deficiency. The results demonstrated that ACL deficiency primarily changed the anterior-posterior motion of the lateral condyle, producing not only posterior subluxation at low flexion positions but also reduced extent of posterior movement during flexion from 15° to 60°.
Three-dimensional tibiofemoral kinematics and femoral condylar motion in ACL-deficient knees during upright weight-bearing flexion were measured using biplane radiography with the geometric center axis.
ACL deficiency caused posterior subluxation of the lateral condyle with excess external femoral rotation at early flexion positions.
On flexion from 15° to 60°, the lateral condyle moved slightly posteriorly in ACL deficiency leading to reduced extent of external femoral rotation.
anterior cruciate ligament; injury; kinematics; tibiofemoral; femoral condyle; radiography
The paper presents the automated segmentation of spleen and liver from contrast-enhanced CT images of normal and hepato/splenomegaly populations. The method used 4 steps: (i) a mean organ model was registered to the patient CT; (ii) the first estimates of the organs were improved by a geodesic active contour; (iii) the contrast enhancements of liver and spleen were estimated to adjust to patient image characteristics, and an adaptive convolution refined the segmentations; (iv) lastly, a normalized probabilistic atlas corrected for shape and location for the precise computation of each organ's volume and height (mid-hepatic liver height and cephalocaudal spleen height). Results from test data demonstrated the method's ability to accurately segment the spleen (RMS error = 1.09mm; DICE/Tanimoto overlaps = 95.2/91) and liver (RMS error = 2.3mm, and DICE/Tanimoto overlaps = 96.2/92.7). The correlations (R2) with clinical/manual height measurements were 0.97 and 0.93 for the spleen and liver respectively.
probabilistic atlas; enhancement estimation; liver; spleen; shape
To provide a tool for quantifying the effects of retinitis pigmentosa (RP) seen on spectral domain optical coherence tomography images, an automated layer segmentation algorithm was developed. This algorithm, based on dual-gradient information and a shortest path search strategy, delineates the inner limiting membrane and three outer retinal boundaries in optical coherence tomography images from RP patients. In addition, an automated inner segment (IS)/outer segment (OS) contour detection method based on the segmentation results is proposed to quantify the locus of points at which the OS thickness goes to zero in a 3D volume scan. The segmentation algorithm and the IS/OS contour were validated with manual segmentation data. The segmentation and IS/OS contour results on repeated measures showed good within-day repeatability, while the results on data acquired on average 22.5 months afterward demonstrated a possible means to follow disease progression. In particular, the automatically generated IS/OS contour provided a possible objective structural marker for RP progression.
(100.0100) Image processing; (170.4470) Ophthalmology; (170.4500) Optical coherence tomography
To develop and validate a multi-dimensional segmentation and filtering methodology for accurate blood flow velocity field reconstruction from phase contrast magnetic resonance imaging (PC MRI).
Materials and Methods
The proposed technique consists of two steps: 1) the boundary of the vessel is automatically segmented using the active contour approach; 2) noise embedded within the segmented vector field is selectively removed using a novel fuzzy adaptive vector median filtering (FAVMF) technique. This two-step segmentation process is tested and validated on 111 synthetically generated PC MRI slices and on 10 patients with congenital heart disease.
The active contour technique was effective for segmenting blood vessels having a sensitivity and specificity of 93.1 and 92.1 % using manual segmentation as a reference standard. FAVMF was the superior technique in filtering out noise vectors, when compared to other commonly used filters in PC MRI (p <0.05). The peak wall shear rate calculated from the PC MRI data (248 ± 39 s−1), was significantly decreased to (146 ± 26 s−1) after the filtering process.
The proposed two step segmentation and filtering methodology is more accurate compared to a single step segmentation process for post processing of PC MRI data.
PC MRI; Noise Filtering; Fuzzy Systems; Vector Median Filtering; Segmentation; Active Contours
To evaluate the performance of surface-based deformable image registration (DR) for adaptive radiotherapy of non-small cell lung cancer (NSCLC).
Based on 13 patients with locally advanced NSCLC, CT images acquired at treatment planning, midway and the end of the radio- (n = 1) or radiochemotherapy (n = 12) course were used for evaluation of DR. All CT images were manually [gross tumor volume (GTV)] and automatically [organs-at-risk (OAR) lung, spinal cord, vertebral spine, trachea, aorta, outline] segmented. Contours were transformed into 3D meshes using the Pinnacle treatment planning system and corresponding mesh points defined control points for DR with interpolation within the structures. Using these deformation maps, follow-up CT images were transformed into the planning images and compared with the original planning CT images.
A progressive tumor shrinkage was observed with median GTV volumes of 170 cm3 (range 42 cm3 - 353 cm3), 124 cm3 (19 cm3 - 325 cm3) and 100 cm3 (10 cm3 - 270 cm3) at treatment planning, mid-way and at the end of treatment. Without DR, correlation coefficients (CC) were 0.76 ± 0.11 and 0.74 ± 0.10 for comparison of the planning CT and the CT images acquired mid-way and at the end of treatment, respectively; DR significantly improved the CC to 0.88 ± 0.03 and 0.86 ± 0.05 (p = 0.001), respectively. With manual landmark registration as reference, DR reduced uncertainties on the GTV surface from 11.8 mm ± 5.1 mm to 2.9 mm ± 1.2 mm. Regarding the carina and intrapulmonary vessel bifurcations, DR reduced uncertainties by about 40% with residual errors of 4 mm to 6 mm on average. Severe deformation artefacts were observed in patients with resolving atelectasis and pleural effusion, in one patient, where the tumor was located around large bronchi and separate segmentation of the GTV and OARs was not possible, and in one patient, where no clear shrinkage but more a decay of the tumor was observed.
The surface-based DR performed accurately for the majority of the patients with locally advanced NSCLC. However, morphological response patterns were identified, where results of the surface-based DR are uncertain.
Examination with CT and image registration is a new technique that we have previously used to assess 3D segmental motions in the lumbar spine in a phantom. Current multi-slice computed tomography (CT) offers highly accurate spatial volume resolution without significant distortion and modern CT scanners makes it possible to reduce the radiation dose to the patients. Our aim was to assess segmental movement in the lumbar spine with the aforementioned method in healthy subjects and also to determine rotation accuracy on phantom vertebrae.
Material and method
The subjects were examined in flexion–extension using low dose CT. Eleven healthy, asymptomatic subjects participated in the current study. The subjects were placed on a custom made jig which could provoke the lumbar spine into flexion or extension. CT examination in flexion and extension was performed. The image analysis was performed using a 3D volume fusion tool, registering one of the vertebrae, and then measuring Euler angles and distances in the registered volumes.
The mean 3D facet joint translation at L4–L5 was in the right facet joint 6.1 mm (3.1–8.3), left facet joint 6.9 mm (4.9–9.9), at L5–S1: right facet joint 4.5 mm (1.4–6.9), and for the left facet joint 4.8 mm (2.0–7.7). In subjects the mean angles at the L4–L5 level were: in the sagittal plane 14.3°, coronal plane 0.9° (−0.6 to 2.8), and in the transverse plane 0.6° (−0.4 to 1.5), in the L5–S1 level the rotation was in sagittal plane 10.2° (2.4–16.1), coronal plane 0° (−1.2 to 1.2), and in the transverse plane 0.2° (−0.7 to 0.3). Repeated analysis for 3D facet joint movement was on average 5 mm with a standard error of mean of 0.6 mm and repeatability of 1.8 mm (CI 95%). For segmental rotation in the sagittal plane the mean rotation was 11.5° and standard error of mean 1°. The repeatability for rotation was 2.8° (CI 95%). The accuracy for rotation in the phantom was in the sagittal plane 0.7°, coronal plane 1°, and 0.7 in the transverse plane.
This method to assess movement in the lumbar spine is a truly 3D method with a high precision giving both visual and numerical output. We believe that this method for measuring spine movement is useful both in research and in clinical settings.
Computed tomography; Joint visualization; 3D-presentation
Objective: To provide a foundation of knowledge concerning the functional anatomy, kinematic response, and mechanisms involved in axial-compression cervical spine injury as they relate to sport injury.
Data Sources: We conducted literature searches through the Index Medicus, SPORT Discus, and PubMed databases and the Library of Congress from 1975–2003 using the key phrases cervical spine injury, biomechanics of cervical spine, football spinal injuries, kinematics of the cervical spine, and axial load.
Data Synthesis: Research on normal kinematics and minor and major injury mechanisms to the cervical spine reveals the complex nature of movement in this segment. The movement into a single plane is not the product of equal and summative movement between and among all cervical vertebrae. Instead, individual vertebrae may experience a reversal of motion while traveling through a single plane of movement. Furthermore, vertebral movement in 1 plane often requires contributed movement in 1 or 2 other planes. Injury mechanisms are even more complex. The reaction of the cervical spine to an axial-load impact has been investigated using cadaver specimens and demonstrates a buckling effect. Impact location and head orientation affect the degree and level of resultant injury.
Conclusions/Recommendations: As with any joint of the body, our understanding of the mechanisms of cervical spine injury will ultimately serve to reduce their occurrence and increase the likelihood of recognition and immediate care. However, the cervical spine is unique in its normal kinematics compared with joints of the extremities. Injury biomechanics in the cervical spine are complex, and much can still be learned about mechanisms of the cervical spine injury specific to sports.
catastrophic injury; whiplash; injury mechanisms; spinal cord; axial load
A pleural effusion is a condition where there is a buildup of abnormal fluid within the pleural space. This paper presents an automated method to evaluate the severity of pleural effusion using regular chest CT images. First the lungs are segmented using region growing, mathematical morphology and anatomical knowledge. Then the visceral and parietal layers of the pleura are extracted based on anatomical landmarks, curve fitting and active contour models. Finally, the pleural space is segmented and the pleural effusion is quantified. Our method was tested on 15 chest CT studies. The automated segmentation is validated against manual tracing and radiologist’s qualitative grading. The Pearson correlation between computer evaluation and radiologist’s grading is 0.956 (P=10−7). The Dice coefficient between the automated and manual segmentation is 0.74±0.07, which is comparable to the variation between two different manual tracings.
Pleural Effusion; CAD; Segmentation
Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmentation. In order to overcome the difficulties caused by intensity inhomogeneities, we propose a region-based active contour model that draws upon intensity information in local regions at a controllable scale. A data fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour. This energy is then incorporated into a variational level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Due to a kernel function in the data fitting term, intensity information in local regions is extracted to guide the motion of the contour, which thereby enables our model to cope with intensity inhomogeneity. In addition, the regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and avoids expensive reinitialization of the evolving level set function. Experimental results for synthetic and real images show desirable performances of our method.
Image segmentation; intensity inhomogeneity; level set method; region-scalable fitting energy; variational method
Analysis of gene expression patterns in brain images obtained from high-throughput in situ hybridization requires accurate and consistent annotations of anatomical regions/subregions. Such annotations are obtained by mapping an anatomical atlas onto the gene expression images through intensity- and/or landmark-based registration methods or deformable model-based segmentation methods. Due to the complex appearance of the gene expression images, these approaches require a pre-processing step to determine landmark correspondences in order to incorporate landmark-based geometric constraints. In this paper, we propose a novel method for landmark-constrained, intensity-based registration without determining landmark correspondences a priori. The proposed method performs dense image registration and identifies the landmark correspondences, simultaneously, using a single higher-order Markov Random Field model. In addition, a machine learning technique is used to improve the discriminating properties of local descriptors for landmark matching by projecting them in a Hamming space of lower dimension. We qualitatively show that our method achieves promising results and also compares well, quantitatively, with the expert’s annotations, outperforming previous methods.
The study design included an in vivo laboratory study. The objective of the study is to quantify the kinematics of the lumbar spinous processes in asymptomatic patients during un-restricted functional body movements with physiological weight bearing. Limited data has been reported on the motion patterns of the posterior spine elements. This information is necessary for the evaluation of traumatic injuries and degenerative changes in the posterior elements, as well as for improving the surgical treatment of spinal diseases using posterior procedures. Eight asymptomatic subjects with an age ranging from 50 to 60 years underwent MRI scans of their lumbar segments in a supine position and 3D models of L2–5 were constructed. Next, each subject was asked to stand and was positioned in the following sequence: standing, 45° flexion, maximal extension, maximal left and right twisting, while two orthogonal fluoroscopic images were taken simultaneously at each of the positions. The MRI models were matched to the osseous outlines of the images from the two orthogonal views to quantify the position of the vertebrae in 3D at each position. The data revealed that interspinous process (ISP) distance decreased from L2 to L3 to L4 to L5 when measured in the supine position; with significantly higher values at L2–3 and L3–4 compared with L4–5. These differences were not seen with weight-bearing conditions. During the maximal extension, the ISP distance at the L2–3 motion segment was significantly reduced, but no significant changes were detected at L3–4 and L4–5. During flexion the ISP distances were not significantly different than those measured in the MRI position at all segments. Going from the left to right twist positions, the L4–5 segment had greater amounts of ISP rotation, while all segments had similar ranges of translation in the transverse plane. The interspinous process distances were dependent on body posture and vertebral level.
Lumbar spine; In vivo; Kinematics; Spinous process; MRI; Fluoroscopy
Active contours are very popular tools for video tracking and image segmentation. Parameterized contours are used due to their fast evolution and have become the method of choice in the Sobolev context. Unfortunately, these contours are not easily adaptable to topological changes, and they may sometimes develop undesirable loops, resulting in erroneous results. To solve such topological problems, one needs an algorithm for contour self-crossing detection. We propose a simple methodology via simple techniques from differential topology. The detection is accomplished by inspecting the total net change of a given contour’s angle, without point sorting and plane sweeping. We discuss the efficient implementation of the algorithm. We also provide algorithms for locating crossings by angle considerations and by plotting the four-connected lines between the discrete contour points. The proposed algorithms can be added to any parametric active-contour model. We show examples of successful tracking in real-world video sequences by Sobolev active contours and the proposed algorithms and provide ideas for further research.
Active contours; image segmentation; self-crossing; snakes; tracking
This study was conducted to evaluate a new method used to calculate vertebra orientation in medical x-ray images. The goal of this work is to develop an x-ray image segmentation approach used to identify the location and the orientation of the cervical vertebrae in medical images. We propose a method for localization of vertebrae by extracting the anterior—left—faces of vertebra contours. This approach is based on automatic corner points of interest detection. For this task, we use the Harris corner detector. The final goal is to determine vertebral motion induced by their movement between two or several positions. The proposed system proceeds in several phases as follows: (a) image acquisition, (b) corner detection, (c) extracting of the corners belonging to vertebra left sides, (d) global estimation of the spine curvature, and (e) anterior face vertebra detection.
Vertebral mobility analysis; corner detection; face contour detection; Harris detector
Prostate cancer is a major health threat for men. For over five years, the U.S. National Cancer Institute has performed prostate biopsies with a magnetic resonance imaging (MRI)-guided robotic system.
A retrospective evaluation methodology and analysis of the clinical accuracy of this system is reported.
Using the pre and post-needle insertion image volumes, a registration algorithm that contains a two-step rigid registration followed by a deformable refinement was developed to capture prostate dislocation during the procedure. The method was validated by using three-dimensional contour overlays of the segmented prostates and the registrations were accurate up to 2 mm.
It was found that tissue deformation was less of a factor than organ displacement. Out of the 82 biopsies from 21 patients, the mean target displacement, needle placement error, and clinical biopsy error was 5.9 mm, 2.3 mm, and 4 mm, respectively.
The results suggest that motion compensation for organ displacement should be used to improve targeting accuracy.
This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model.
A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed.
The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6 mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10 % in most of the organs considered.
The proposed automated quantification technique is reliable, robust and suitable for fast quantification of preclinical PET data in large serial studies.
PET/CT; Small animals; Quantification; Deformable registration; Atlas