Anatomical model matching with fuzzy implicit surfaces for segmentation of thoracic volume scans

Implementation and prospective clinical validation of AI-based planning and shimming techniques in cardiac MRI

PURPOSE

Cardiovascular magnetic resonance (CMR) is a vital diagnostic tool in the management of cardiovascular diseases. The advent of advanced CMR technologies combined with artificial intelligence (AI) has the potential to simplify imaging, reduce image acquisition time without compromising image quality (IQ), and improve magnetic field uniformity. Here, we aim to implement two AI-based deep learning techniques for automatic slice alignment and cardiac shimming and evaluate their performance in clinical cardiac magnetic resonance imaging (MRI).

METHODS

Two deep neural networks were developed, trained, and validated on pre-acquired cardiac MRI datasets (>500 subjects) to achieve automatic slice planning and shimming (implemented in the scanner) for CMR. To examine the performance of our automated cardiac planning (EasyScan) and AI-based shim (AI shim), two prospective studies were performed subsequently. For the EasyScan validation, 10 healthy subjects underwent two identical CMR protocols: with manual cardiac planning and with AI-based EasyScan to assess protocol scan time difference and accuracy of cardiac plane prescriptions on a 1.5 T clinical MRI scanner. For the AI shim validation, a total of 20 subjects were recruited: 10 healthy and 10 cardio-oncology patients with referrals for a CMR examination. Cine images were obtained with standard cardiac volume shim and with AI shim to assess signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall IQ (sharpness and MR image degradation), ejection fraction (EF), and absolute wall thickening. A hybrid statistical method using of nonparametric (Wilcoxon) and parametric (t-test) assessments was employed for statistical analyses.

RESULTS

CMR protocol with AI-based plane prescriptions, EasyScan, minimized operator dependence and reduced overall scanning time by over 2 min (∼13 % faster, p < 0.001) compared to the protocol with manual cardiac planning. EasyScan plane prescriptions also demonstrated more accurate (less plane angulation errors from planes manually prescribed by a certified cardiac MRI technologist) cardiac planes than previously reported strategies. Additionally, AI shim resulted in improved B0 field homogeneity. Cine images obtained with AI shim revealed a significantly higher SNR (12.49%; p = 0.002) than those obtained with volume shim (volume shim: 32.90 ± 7.42 vs. AI shim: 37.01 ± 8.87) for the left ventricle (LV) myocardium. LV myocardium CNR was 12.48% higher for cine imaging with AI shim (149.02 ± 39.15) than volume shim (132.49 ± 33.94). Images obtained with AI shim resulted in sharper images than those obtained with volume shim (p = 0.012). The LVEF and absolute wall thickening also showed that differences exist between the two shimming methods. The LVEF by AI shim was shown to be slightly larger than LVEF by volume shim in two groups: 2.87% higher with AI shim for the healthy group and 1.70% higher with AI shim for the patient group. The LV absolute wall thickening (in mm) also showed that differences exist between shimming methods for each group with larger changes observed in the patient group (healthy: 3.31%, p = 0.234 and patient group: 7.29%, p = 0.059).

CONCLUSIONS

CMR exams using EasyScan for cardiac planning demonstrated accelerated cardiac exam compared to the CMR protocol with manual cardiac planning. Improved and more uniform B0 magnetic field homogeneity also achieved using AI shim technique compared to volume shimming.

Robust automated constellation-based landmark detection in human brain imaging

A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses the knowledge of each additional detected landmark as an improved foundation for improved prediction of the next landmark location. The accuracy and robustness of the presented method was evaluated by comparing the automatically generated results to two manual raters on 30 identified landmark points extracted from each of 30 T1-weighted magnetic resonance images. For the landmarks with unambiguous anatomical definitions, the average discrepancy between the algorithm results and each human observer differed by less than 1 mm from the average inter-observer variability when the algorithm was evaluated on imaging data collected from the same site as the model building data. Similar results were obtained when the same model was applied to a set of heterogeneous image volumes from seven different collection sites representing 3 scanner manufacturers. This method is reliable for general application in large-scale multi-site studies that consist of a variety of imaging data with different orientations, spacings, origins, and field strengths.

Anatomical pulmonary magnetic resonance imaging segmentation for regional structure-function measurements of asthma

PURPOSE

Pulmonary magnetic-resonance-imaging (MRI) and x-ray computed-tomography have provided strong evidence of spatially and temporally persistent lung structure-function abnormalities in asthmatics. This has generated a shift in their understanding of lung disease and supports the use of imaging biomarkers as intermediate endpoints of asthma severity and control. In particular, pulmonary (1)H MRI can be used to provide quantitative lung structure-function measurements longitudinally and in response to treatment. However, to translate such biomarkers of asthma, robust methods are required to segment the lung from pulmonary (1)H MRI. Therefore, their objective was to develop a pulmonary (1)H MRI segmentation algorithm to provide regional measurements with the precision and speed required to support clinical studies.

METHODS

The authors developed a method to segment the left and right lung from (1)H MRI acquired in 20 asthmatics including five well-controlled and 15 severe poorly controlled participants who provided written informed consent to a study protocol approved by Health Canada. Same-day spirometry and plethysmography measurements of lung function and volume were acquired as well as (1)H MRI using a whole-body radiofrequency coil and fast spoiled gradient-recalled echo sequence at a fixed lung volume (functional residual capacity + 1 l). We incorporated the left-to-right lung volume proportion prior based on the Potts model and derived a volume-proportion preserved Potts model, which was approximated through convex relaxation and further represented by a dual volume-proportion preserved max-flow model. The max-flow model led to a linear problem with convex and linear equality constraints that implicitly encoded the proportion prior. To implement the algorithm, (1)H MRI was resampled into ∼3 × 3 × 3 mm(3) isotropic voxel space. Two observers placed seeds on each lung and on the background of 20 pulmonary (1)H MR images in a randomized dataset, on five occasions, five consecutive days in a row. Segmentation accuracy was evaluated using the Dice-similarity-coefficient (DSC) of the segmented thoracic cavity with comparison to five-rounds of manual segmentation by an expert observer. The authors also evaluated the root-mean-squared-error (RMSE) of the Euclidean distance between lung surfaces, the absolute, and percent volume error. Reproducibility was measured using the coefficient of variation (CoV) and intraclass correlation coefficient (ICC) for two observers who repeated segmentation measurements five-times.

RESULTS

For five well-controlled asthmatics, forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) was 83% ± 7% and FEV1 was 86 ± 9%pred. For 15 severe, poorly controlled asthmatics, FEV1/FV C = 66% ± 17% and FEV1 = 72 ± 27%pred. The DSC for algorithm and manual segmentation was 91% ± 3%, 92% ± 2% and 91% ± 2% for the left, right, and whole lung, respectively. RMSE was 4.0 ± 1.0 mm for each of the left, right, and whole lung. The absolute (percent) volume errors were 0.1 l (∼6%) for each of right and left lung and ∼0.2 l (∼6%) for whole lung. Intra- and inter-CoV (ICC) were <0.5% (>0.91%) for DSC and <4.5% (>0.93%) for RMSE. While segmentation required 10 s including ∼6 s for user interaction, the smallest detectable difference was 0.24 l for algorithm measurements which was similar to manual measurements.

CONCLUSIONS

This lung segmentation approach provided the necessary and sufficient precision and accuracy required for research and clinical studies.

Globally optimal co-segmentation of three-dimensional pulmonary ¹H and hyperpolarized ³He MRI with spatial consistence prior

Pulmonary imaging using hyperpolarized (3)He/(129)Xe gas is emerging as a new way to understand the regional nature of pulmonary ventilation abnormalities in obstructive lung diseases. However, the quantitative information derived is completely dependent on robust methods to segment both functional and structural/anatomical data. Here, we propose an approach to jointly segment the lung cavity from (1)H and (3)He pulmonary magnetic resonance images (MRI) by constraining the spatial consistency of the two segmentation regions, which simultaneously employs the image features from both modalities. We formulated the proposed co-segmentation problem as a coupled continuous min-cut model and showed that this combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In particular, we introduced a dual coupled continuous max-flow model to study the convex relaxed coupled continuous min-cut model under a primal and dual perspective. This gave rise to an efficient duality-based convex optimization algorithm. We implemented the proposed algorithm in parallel using general-purpose programming on graphics processing unit (GPGPU), which substantially increased its computational efficiency. Our experiments explored a clinical dataset of 25 subjects with chronic obstructive pulmonary disease (COPD) across a wide range of disease severity. The results showed that the proposed co-segmentation approach yielded superior performance compared to single-channel image segmentation in terms of precision, accuracy and robustness.

Automatic slice alignment method for cardiac magnetic resonance imaging

OBJECTIVES

Automatic slice alignment is important for easier operation and shorter examination times in cardiac magnetic resonance imaging (MRI) examinations. We propose a new automatic slice alignment method for six cardiac planes (short-axis, vertical long-axis, horizontal long-axis, 4-chamber, 2-chamber, and 3-chamber views).

MATERIALS AND METHODS

ECG-gated 2D steady-state free precession axial multislice images were acquired using a 1.5-T MRI scanner during a single breath-hold. The scanning time was set to <20 s in 23 volumes from 23 healthy volunteers. In this method, the positions of the mitral valve, cardiac apex, left ventricular outflow tract, tricuspid valve, anterior wall of the heart, and right ventricular corner are detected to determine the positions of six reference planes by combining knowledge-based recognition and image processing techniques. In order to evaluate the results of automatic slice alignment for the short-axis, 4-chamber, 2-chamber, and 3-chamber views, the angular and positional errors between the results obtained by our proposed method and by manual annotation were measured.

RESULTS

The average angular errors for the short-axis, 4-chamber, 2-chamber, and 3-chamber views were 3.05°, 4.52°, 7.28°, and 5.79°, respectively. The average positional errors for the short-axis (base), short-axis (apex), 4-chamber, 2-chamber, and 3-chamber views were 6.61°, 3.80°, 1.55°, 1.52°, and 1.48°, respectively.

CONCLUSION

The experimental results showed that our proposed method can detect the cardiac planes quickly and accurately. Our method is therefore beneficial to both patients and operators.

Detection of the whole myocardium in 2D-echocardiography for multiple orientations using a geometrically constrained level-set

The segmentation of the myocardium in echocardiographic images is an important task for the diagnosis of heart disease. This task is difficult due to the inherent problems of echographic images (i.e. low contrast, speckle noise, signal dropout, presence of shadows). In this article, we propose a method to segment the whole myocardium (endocardial and epicardial contours) in 2D echographic images. This is achieved using a level-set model constrained by a new shape formulation that allows to model both contours. The novelty of this work also lays in the fact that our framework allows to segment the whole myocardium for the four main views used in clinical routine. The method is validated on a dataset of clinical images and compared with expert segmentation.

Quantitative analysis of tumor burden in mouse lung via MRI

Lung cancer is the leading cause of cancer death in the United States. Despite recent advances in screening protocols, the majority of patients still present with advanced or disseminated disease. Preclinical rodent models provide a unique opportunity to test novel therapeutic drugs for targeting lung cancer. Respiratory-gated MRI is a key tool for quantitatively measuring lung-tumor burden and monitoring the time-course progression of individual tumors in mouse models of primary and metastatic lung cancer. However, quantitative analysis of lung-tumor burden in mice by MRI presents significant challenges. Herein, a method for measuring tumor burden based upon average lung-image intensity is described and validated. The method requires accurate lung segmentation; its efficiency and throughput would be greatly aided by the ability to automatically segment the lungs. A technique for automated lung segmentation in the presence of varying tumor burden levels is presented. The method includes development of a new, two-dimensional parametric model of the mouse lungs and a multi-faceted cost function to optimally fit the model parameters to each image. Results demonstrate a strong correlation (0.93), comparable with that of fully manual expert segmentation, between the automated method's tumor-burden metric and the tumor burden measured by lung weight.

Advances in Attenuation Correction Techniques in PET

Molecular imaging using PET has evolved from a vigorous academic field into the clinical arena. Considerable advances have been made in the design of high-resolution standalone PET and combined PET/CT units dedicated to clinical whole-body scanning. Likewise, much worthwhile research focused on the development of quantitative imaging protocols incorporating accurate data correction techniques and sophisticated image reconstruction algorithms. Since its inception, photon attenuation in biological tissues has been identified as the most important physical degrading factor affecting PET image quality and quantitative accuracy. Various strategies have been devised to determine an accurate attenuation map to enable correction for nonlinear photon attenuation in whole-body PET studies. This article presents the physical and methodological basis of photon attenuation and summarizes state-of-the-art developments in algorithms used to derive the attenuation map aiming at accurate attenuation compensation of PET data. Future prospects, research trends, and challenges are identified, and directions for future research are discussed.

Automated lung segmentation of diseased and artifact-corrupted magnetic resonance sections

Segmentation of the lungs within magnetic resonance (MR) scans is a necessary step in the computer-based analysis of thoracic MR images. This process is often confounded by image acquisition artifacts and disease-induced morphological deformation. We have developed an automated method for lung segmentation that is insensitive to these complications. The automated method was applied to 23 thoracic MR scans (413 sections) obtained from 10 patients. Two radiologists manually outlined the lung regions in a random sample of 101 sections (n=202 lungs), and the extent to which disease or artifact confounded lung border visualization was evaluated. Accuracy of lung regions extracted by the automated segmentation method was quantified by comparison with the radiologist-defined lung regions using an area overlap measure (AOM) that ranged from 0 (disjoint lung regions) to 1 (complete overlap). The AOM between each observer and the automated method was 0.82 when averaged over all lungs. The average AOM in the lung bases, where lung segmentation is most difficult, was 0.73.

A comprehensive shape model of the heart

Domain knowledge about the geometrical properties of cardiac structures is an important ingredient for the segmentation of these structures in medical images or for the simulation of cardiac physiology. So far, a strong focus was put on the left ventricle due to its importance for the general pumping performance of the heart and related functional indices. However, other cardiac structures are of similar importance, e.g., the coronary arteries with respect to diagnosis and treatment of arteriosclerosis or the left atrium with respect to the treatment of atrial fibrillation. In this paper we describe the generation of a geometric cardiac model including the four cardiac chambers and the trunks of the connected vasculature, as well as the coronary arteries and a set of cardiac landmarks. A mean geometric model for the end-diastolic heart has been built based on 27 cardiac CT datasets and has been evaluated with respect to its capability to estimate the position of cardiac structures. Allowing a similarity transformation to adapt the model to image data, cardiac surface positions can be predicted with an accuracy of below 5mm.

Influence of positional and angular variation of automatically planned short-axis stacks on quantification of left ventricular dimensions and function with cardiovascular magnetic resonance

PURPOSE

To theoretically and experimentally investigate the influence of the automated cardiovascular magnetic resonance (CMR) scan planning pitfalls, namely inaccurate positioning and tilting of short-axis (SA) imaging planes, on quantification of the left ventricular (LV) dimensions and function.

MATERIALS AND METHODS

Eleven healthy subjects and eight patients underwent CMR. Manually and automatically planned SA sets were acquired. To obtain the quantitative measurements of LV function, one observer performed image analysis twice. The agreement between planning methods, as well as the decomposition of the total variation into interstudy and intraobserver components was measured.

RESULTS

The decomposition of the total variation showed that the interstudy factor accounts for 70-85% of the total variation, while the rest is due to the intraobserver factor. Moreover, the relative contribution of the interstudy factor remains independent from errors in slice positioning and small angular deviation of SA stacks from the optimal orientation. Good agreement between the theoretical and measured variability factors was observed.

CONCLUSION

Global LV function derived from the automatically planned CMR acquisitions yield accurate quantification of the human cardiovascular system. Inaccurate positioning and tilting of SA images does not affect the quantitative measurements of LV function. The computer-aided system for automated CMR has proven clinical applicability.

Accuracy of short-axis cardiac MRI automatically derived from scout acquisitions in free-breathing and breath-holding modes

To qualitatively assess the accuracy of automated cardiovascular magnetic resonance planning procedures devised from scout acquisitions in free-breathing and breath-holding modes, to quantitatively evaluate the accuracy of the derived left ventricular volumes, mass and function and compare these parameters with the ones obtained from the manually planned acquisitions. Ten healthy volunteers underwent cardiovascular MR (CMR) acquisitions for ventricular function assessment. Short-axis data sets of the left ventricle (LV) were manually planned and generated twice in an automatic fashion. Automated planning parameters were derived from gated scout acquisitions in free-breathing and breath-holding modes. End-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and left ventricular mass (LVM) were measured. The agreement between the manual and automatic planning methods, as well as the variability of the aforementioned measurements were assessed. The differences between two automated planning methods were also compared. The mean differences between the manual and automated CMR planning derived from gated scouts in free-breathing mode were 8.05 ml (EDV), 1.84 ml (ESV), 0.69% (EF), and 4.72 g (LVM). The comparison between manual and automated CMR planning derived from gated scouts in breath-holding mode yielded the following differences: 4.22 ml (EDV), 0.34 ml (ESV), 0.3% (EF), and -0.72 mg (LVM). The variability coefficients were 3.72 and 3.66 (EDV), 5.6 and 8.19 (ESV), 3.46 and 4.31 (EF), 6.49 and 5.20 (LVM) for the automated CMR planning methods derived from scouts in free-breathing and breath-holding modes, respectively. Automated CMR planning methods can provide accurate measurements of LV dimensions in normal subjects, and therefore may be utilized in the clinical environment to provide a cost-effective solution for functional assessment of the human cardiovascular system.

Automated Short-Axis Cardiac Magnetic Resonance Image Acquisitions

RATIONALE AND OBJECTIVE

This study investigates the use of an automated observer-independent planning system for short-axis cardiovascular magnetic resonance (MR) acquisitions in the clinical environment. The capacity of the automated method to produce accurate measurements of left ventricular dimensions and function was quantitatively assessed in normal subjects and patients.

METHODS

Fourteen healthy volunteers and 8 patients underwent cardiovascular MR (CMR) acquisitions for ventricular function assessment. Short-axis datasets of the left ventricle (LV) were acquired in 2 ways: manually planned and generated in an automatic fashion. End-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and left ventricular mass (LVM) were derived from the 2 datasets. The agreement between the manual and automatic planning methods was assessed.

RESULTS

The mean differences between the manual and automated CMR planning methods for the normal subjects and patients were 5.89 mL and 1.93 mL (EDV), 1.14 mL and -0.41 mL (ESV), 0.81% and 0.89% (EF), and 4.35 g and 3.88 g (LVM), respectively. There was no significant difference in ESV and EF. LVM significantly differed in both groups, whereas EDV was significantly different in the normal subjects and insignificantly different in the patients. The variability coefficients were 2.8 and 3.59 (EDV), 3.3 and 5.03 (ESV), 1.79 and 2.65 (EF), and 4.36 and 2.27 (LVM) for the normal subjects and patients, respectively. The mean angular deviation of the LV axes turned out to be 8.58 +/- 5.76 degrees for the normal subjects and 8.35 +/- 5.15 degrees for the patients.

CONCLUSIONS

Automated CMR planning method can provide accurate measurements of LV dimensions in normal subjects and patients, and therefore, can be used in the clinical environment for functional assessment of the human cardiovascular system.

Landmark detection in the chest and registration of lung surfaces with an application to nodule registration

We developed an automated system for registering computed tomography (CT) images of the chest temporally. Our system detects anatomical landmarks, in particular, the trachea, sternum and spine, using an attenuation-based template matching approach. It computes the optimal rigid-body transformation that aligns the corresponding landmarks in two CT scans of the same patient. This transformation then provides an initial registration of the lung surfaces segmented from the two scans. The initial surface alignment is refined step by step in an iterative closest-point (ICP) process. To establish the correspondence of lung surface points, Elias' nearest neighbor algorithm was adopted. Our method improves the processing time of the original ICP algorithm from O(kn log n) to O(kn), where k is the number of iterations and n the number of surface points. The surface transformation is applied to align nodules in the initial CT scan with nodules in the follow-up scan. For 56 out of 58 nodules in the initial CT scans of 10 patients, nodule correspondences in the follow-up scans are established correctly. Our methods can therefore potentially facilitate the radiologist's evaluation of pulmonary nodules on chest CT for interval growth.

Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling

A novel method is introduced for the generation of landmarks for three-dimensional (3-D) shapes and the construction of the corresponding 3-D statistical shape models. Automatic landmarking of a set of manual segmentations from a class of shapes is achieved by 1) construction of an atlas of the class, 2) automatic extraction of the landmarks from the atlas, and 3) subsequent propagation of these landmarks to each example shape via a volumetric nonrigid registration technique using multiresolution B-spline deformations. This approach presents some advantages over previously published methods: it can treat multiple-part structures and requires less restrictive assumptions on the structure's topology. In this paper, we address the problem of building a 3-D statistical shape model of the left and right ventricle of the heart from 3-D magnetic resonance images. The average accuracy in landmark propagation is shown to be below 2.2 mm. This application demonstrates the robustness and accuracy of the method in the presence of large shape variability and multiple objects.

Automated observer-independent acquisition of cardiac short-axis MR images: a pilot study

The authors compared an automated observer-independent acquisition planning method for short-axis multisection multiphase cardiac magnetic resonance imaging studies with conventional manual image planning. Systematic and random differences and reproducibility of left ventricular function measurements and image geometry were evaluated in five healthy adult volunteers and 20 patient studies. Results with the automated planning method were as accurate and reproducible as those with the manual planning method.

Three-dimensional modeling for functional analysis of cardiac images: a review

Three-dimensional (3-D) imaging of the heart is a rapidly developing area of research in medical imaging. Advances in hardware and methods for fast spatio-temporal cardiac imaging are extending the frontiers of clinical diagnosis and research on cardiovascular diseases. In the last few years, many approaches have been proposed to analyze images and extract parameters of cardiac shape and function from a variety of cardiac imaging modalities. In particular, techniques based on spatio-temporal geometric models have received considerable attention. This paper surveys the literature of two decades of research on cardiac modeling. The contribution of the paper is three-fold: 1) to serve as a tutorial of the field for both clinicians and technologists, 2) to provide an extensive account of modeling techniques in a comprehensive and systematic manner, and 3) to critically review these approaches in terms of their performance and degree of clinical evaluation with respect to the final goal of cardiac functional analysis. From this review it is concluded that whereas 3-D model-based approaches have the capability to improve the diagnostic value of cardiac images, issues as robustness, 3-D interaction, computational complexity and clinical validation still require significant attention.

Quantification of global and regional ventricular function in cardiac magnetic resonance imaging

One of the strong assets of cardiac magnetic resonance (CMR) is its ability to assess myocardial anatomy, structure, function, flow, and perfusion within a single examination. Quantification of global and regional function from magnetic resonance imaging (MRI) studies was shown to be accurate and reproducible in experimental and clinical research studies. With the advent of high-performance MRI scanners and newly developed pulse sequences, image acquisition times have been reduced considerably in recent years. However, the clinical use of CMR remains limited for various reasons. Among these limitations is that the amount of images obtained in a typical cardiac examination is so large that visual and especially quantitative image analysis is tedious and time consuming. There is an urgent need for optimized dedicated software tools featuring highly automated contour detection and optimized display capabilities to present the quantitative results to the physician in an orderly fashion, thus facilitating clinical decision making. This article focuses on the state of the art in CMR postprocessing techniques for quantitative assessment of global and regional function.

Quantification in cardiac MRI

Magnetic resonance imaging (MRI) offers several acquisition techniques for precise and highly reproducible assessment of global and regional ventricular function, flow, and perfusion at rest and under pharmacological or physical stress conditions. Recent advances in hardware and software have resulted in strong improvement of image quality and in a significant decrease in the required imaging time for each of these acquisitions. Several aspects of heart disease can be studied by combining multiple MRI techniques in a single examination. Such a comprehensive examination could replace a number of other imaging procedures, such as diagnostic X-ray angiography, echocardiography, and scintigraphy, which would be beneficial for the patient and cost effective. Despite the advances in MRI, quantitative image analysis often still relies on manual tracing of contours in the images, which is a time-consuming and tedious procedure that limits the clinical applicability of cardiovascular MRI. Reliable automated or semi-automated image analysis software would be very helpful to overcome the limitations associated with manual image processing. In this paper the developments directed toward automated quantitative image analysis and semi-automated contour detection for cardiovascular MR imaging are reviewed. J. Magn. Reson. Imaging 1999; 10:602-608.