Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm

RATIONALE AND OBJECTIVE: This article presents the evaluation of an interactive multiscale watershed segmentation algorithm for segmenting tumors in magnetic resonance brain images of patients scheduled for neuronavigational procedures. MATERIALS AND METHODS: The watershed method is compared with manual delineation with respect to accuracy, repeatability, and efficiency. RESULTS: In the 20 patients included in this study, the measured volume of the tumors ranged from 2.7 to 81.9 cm(3). A comparison of the tumor volumes measured with watershed segmentation to the volumes measured with manual delineation shows that the two methods are interchangeable according to the Bland and Altman criterion, and thus equally accurate. The repeatability of the watershed method and the manual method are compared by looking at the similarity of the segmented volumes. The similarity for intraobserver and interobserver variability for watershed segmentation is 96.4% and 95.3%, respectively, compared with 93.5% and 90.0% for manual outlining, from which it may be concluded that the watershed method is more repeatable. Moreover, the watershed algorithm is on average three times faster than manual outlining. CONCLUSION: The watershed method has an accuracy comparable to that of manual delineation and outperforms manual outlining on the criteria of repeatability and efficiency.     

Unsupervised Measurement of Brain Tumor Volume on MR Images

We examined unsupervised methods of segmentation of MR images of the brain for measuring tumor volume in response to treatment. Two clustering methods were used: fuzzy c-means and a nonfuzzy clustering algorithm. Results were compared with volume segmentations by two supervised methods, k-nearest neighbors and region growing, and all results were compared with manual labelings. Results of individual segmentations are presented as well as comparisons on the application of the different methods with 10 data sets of patients with brain tumors. Unsupervised segmentation is preferred for measuring tumor volumes in response to treatment, as it eliminates operator dependency and may be adequate for delineation of the target volume in radiation therapy. Some obstacles need to be overcome, in particular regard-big the detection of anatomically relevant tissue classes. This study shows that these improvements are possible.     

3D airway segmentation via hyperpolarized 3He gas MRI by using scale-based fuzzy connectedness.

Computerized segmentation of 3D tracheobronchial tree is a necessary first step for subsequent registration and analysis of pulmonary airway and vascular magnetic resonance (MR) images obtained by using hyperpolarized 3Helium gas and Gadolinium. The scientific and clinical implications of acquiring these data on the tracheobronchial tree (for studying ventilation, V) and on the coinciding pulmonary arterioles (for studying perfusion, Q), is the next frontier for static and dynamic pulmonary MRI. In this paper, we report an airway segmentation method from 3He MR images based on the scale-based fuzzy connectedness approach. Incorporated in this method are the pre-processing steps of inhomogeneity correction and intensity standardization. The basic sequential steps in the proposed airway segmentation method are: (1) image acquisition, (2) radio frequency field inhomogeneity correction, (3) standardization of MR image intensity scale, (4) seed specification, (5) scale-based fuzzy connected segmentation of airways, and (6) thresholding and binarization. The majority of these steps are automatically executed; others allow interaction through a graphical interface provided in the 3DVIEWNIX software system, in which the algorithms are implemented. The method achieves an overall precision of about 98% in terms of the extent of overlap in repeated segmentations. Its level of accuracy can be described by a true positive volume fraction of about 98% (considering manual delineation as the surrogate of true delineation), and a false negative and positive volume fraction of about 1%. The total operator and computational time required per study are on the average 2 and 20 min.     

A system for brain tumor volume estimation via MR imaging and fuzzy connectedness.

This paper presents a method for the precise, accurate and efficient quantification of brain tumor (glioblastomas) via MRI that can be used routinely in the clinic. Tumor volume is considered useful in evaluating disease progression and response to therapy, and in assessing the need for changes in treatment plans. We use multiple MRI protocols including FLAIR, T1, and T1 with Gd enhancement to gather information about different aspects of the tumor and its vicinity. These include enhancing tissue, nonenhancing tumor, edema, and combinations of edema and tumor. We have adapted the fuzzy connectedness framework for tumor segmentation in this work and the method requires only limited user interaction in routine clinical use. The system has been tested for its precision, accuracy, and efficiency, utilizing 10 patient studies. The percent coefficient of variation (% CV) in volume due to operator subjectivity in specifying seeds for fuzzy connectedness segmentation is less than 1%. The mean operator and computer time required per study for estimating the volumes of both edema and enhancing tumor is about 16 min. The software package is designed to run under operator supervision. Delineation has been found to agree with the operators' visual inspection most of the time except in some cases when the tumor is close to the boundary of the brain. In the latter case, the scalp, surgical scar, or orbital contents are included in the delineation, and an operator has to exclude this manually. The methodology is rapid, robust, consistent, yielding highly reproducible measurements, and is likely to become part of the routine evaluation of brain tumor patients in our health system.     

基于卷积神经网络的深度学习算法对颅内出血的类型识别及血肿分割一致性的研究

目的:开发一种可以检测不同类型颅内出血并自动计算血肿体积的基于卷积神经网络的深度学习算法,探讨其识别的准确性及血肿分割的一致性.方法:数据集1纳入9594例颅脑CT平扫图像,随机选取223例颅内出血阳性患者作为颅内出血类型识别的测试集,剩余CT图像作为其训练集,评估测试集中算法识别五种不同类型颅内出血的效能.数据集2选...     

Free parameter, figure of merit and ionization quench in liquid scintillation counting.

A statistical study of the detection process demonstrates that the free parameter is essential to compute the counting efficiency in both CIEMAT/NIST and TDCR methods. An analysis of the computed counting efficiencies shows the uselessness of old definition of the figure of merit. A new definition is required and we adopt the idea of taking quantities related with the output of the photomultiplier. In addition, we justify the application of the chemical quenching simulation with the electronic variation of the photomultiplier gain. Finally, we describe a new procedure to determine the figure of merit and the optimum ionization-quenching factor from the pulse spectrum of different radionuclides. The robustness of the new procedure is tested with three different sets of stopping power for low-energy electrons.     

Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy

RATIONALE AND OBJECTIVES: The segmentation of airways from CT images is a critical first step for numerous virtual bronchoscopic (VB) applications. Automatic or semiautomatic methods are necessary, since manual segmentation is prohibitively time consuming. The methods must be robust and operate within a reasonable time frame to be useful for clinical VB use. The authors developed an integrated airway segmentation system and demonstrated its effectiveness on a series of human images. MATERIALS AND METHODS: The authors' airway segmentation system draws on two segmentation algorithms: (a) an adaptive region-growing algorithm and (b) a new hybrid algorithm that uses both region growing and mathematical morphology. Images from an ongoing VB study were segmented by means of both the adaptive region-growing and the new hybrid methods. The segmentation volume, branch number estimate, and segmentation quality were determined for each case. RESULTS: The results demonstrate the need for an integrated segmentation system, since no single method is superior for all clinically relevant cases. The region-growing algorithm is the fastest and provides acceptable segmentations for most VB applications, but the hybrid method provides superior airway edge localization, making it better suited for quantitative applications. In addition, the authors show that prefiltering the image data before airway segmentation increases the robustness of both region-growing and hybrid methods. CONCLUSION: The combination of these two algorithms with the prefiltering options allowed the successful segmentation of all test images. The times required for all segmentations were acceptable, and the results were suitable for the authors' VB application needs.     

Application of a new segmentation tool based on interactive simplex meshes to cardiac images and pulmonary MRI data

RATIONALE AND OBJECTIVES: Medical image segmentation is still very time consuming and is therefore seldom integrated into clinical routine. Various three-dimensional (3D) segmentation approaches could facilitate the work, but they are rarely used in clinical setups because of complex initialization and parametrization of such models. MATERIALS AND METHODS: We developed a new semiautomatic 3D-segmentation tool based on deformable simplex meshes. The user can define attracting points in the original image data. The new deformation algorithm guarantees that the surface model will pass through these interactively set points. The user can directly influence the evolution of the deformable model and gets direct feedback during the segmentation process. RESULTS: The segmentation tool was evaluated for cardiac image data and magnetic resonance imaging lung images. Comparison with manual segmentation showed high accuracy. Time needed for delineation of the various structures could be reduced in some cases. The model was not sensitive to noise in the input data and model initialization. CONCLUSIONS: The tool is suitable for fast interactive segmentation of any kind of 3D or 3D time-resolved medical image data. It enables the clinician to influence a complex 3D-segmentation algorithm and makes this algorithm controllable. The better the quality of the data, the less interaction is required. The tool still works when the processed images have low quality.     

Automatic segmentation and recognition of lungs and lesion from CT scans of thorax

In this study, a fully automated texture-based segmentation and recognition system for lesion and lungs from CT of thorax is presented. For the segmentation part, we have extracted texture features by Gabor filtering the images, and, then combined these features to segment the target volume by using Fuzzy C Means (FCM) clustering. Since clustering is sensitive to initialization of cluster prototypes, optimal initialization of the cluster prototypes was done by using a Genetic Algorithm.For the recognition stage, we have used cortex like mechanism for extracting statistical features in addition to shape-based features. The segmented regions showed a high degree of imbalance between positive and negative samples, so we employed over and under sampling for balancing the data. Finally, the balanced and normalized data was subjected to Support Vector Machine (SimpleSVM) for training and testing.Results reveal an accuracy of delineation to be 94.06%, 94.32% and 89.04% for left lung, right lung and lesion, respectively. Average sensitivity of the SVM classifier was seen to be 89.48%.     

Development of automated segmentation method for the posterior portion of the temporal lobe on coronal MR images

Brain MRI is an important method for examining the diseases caused by various cerebral pathologies, and the measurement of temporal lobe volume is useful for identifying dementia and temporal lobe abnormalities. However, no segmentation algorithm for the temporal lobe on coronal MR images has been established. Such an algorithm is needed because the shape of the temporal lobe on coronal images varies from area to area. The purpose of this research was to develop a segmentation method for the posterior portion of the temporal lobe on coronal MR images. The subjects were 11 normal patients, whose coronal T(1)-weighted images were selected for this study. The preprocessing algorithm for segmentation consists of smoothing, binarization, and thinning. The first step of the segmentation process consists of recognition techniques for the temporal lobe region on thinning images. The next step is distance transformation on identified thinning images. Finally, the temporal lobe was segmented by using the original images and distance transformation images and employing the newly developed algorithm. The rate of accuracy of automated recognition was over 74% for all cases, while the average rate of accuracy was 83.2+/-4.0%. These results suggest that this segmentation method can clearly segment the temporal lobe and has potential for clinical use. Based on this study, although it included only 11 normal patients, we have started applying this segmentation method to many patients, with or without temporal lobe disease.     

Evaluation of uterine cervix segmentations using ground truth from multiple experts

This work is focused on the generation and utilization of a reliable ground truth (GT) segmentation for a large medical repository of digital cervicographic images (cervigrams) collected by the National Cancer Institute (NCI). NCI invited twenty experts to manually segment a set of 939 cervigrams into regions of medical and anatomical interest. Based on this unique data, the objectives of the current work are to: (1) Automatically generate a multi-expert GT segmentation map; (2) Use the GT map to automatically assess the complexity of a given segmentation task; (3) Use the GT map to evaluate the performance of an automated segmentation algorithm.The multi-expert GT map is generated via the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm, which is a well-known method to generate a GT segmentation from multiple observations. A new measure of segmentation complexity, which relies on the inter-observer variability within the GT map, is defined. This measure is used to identify images that were found difficult to segment by the experts and to compare the complexity of different segmentation tasks. An accuracy measure, which evaluates the performance of automated segmentation algorithms is presented. Two algorithms for cervix boundary detection are compared using the proposed accuracy measure. The measure is shown to reflect the actual segmentation quality achieved by the algorithms.The methods and conclusions presented in this work are general and can be applied to different images and segmentation tasks. Here they are applied to the cervigram database including a thorough analysis of the available data.     

Early clinical evaluation of a novel three-dimensional structure delineation software tool (SCULPTER) for radiotherapy treatment planning

Modern radiotherapy treatment planning (RTP) necessitates increased delineation of target volumes and organs at risk. Conventional manual delineation is a laborious, time-consuming and subjective process. It is prone to inconsistency and variability, but has the potential to be improved using automated segmentation algorithms. We carried out a pilot clinical evaluation of SCULPTER (Structure Creation Using Limited Point Topology Evidence in Radiotherapy) - a novel prototype software tool designed to improve structure delineation for RTP. Anonymized MR and CT image datasets from patients who underwent radiotherapy for bladder or prostate cancer were studied. An experienced radiation oncologist used manual and SCULPTER-assisted methods to create clinically acceptable organ delineations. SCULPTER was also tested by four other RTP professionals. Resulting contours were compared by qualitative inspection and quantitatively by using the volumes of the structures delineated and the time taken for completion. The SCULPTER tool was easy to apply to both MR and CT images and diverse anatomical sites. SCULPTER delineations closely reproduced manual contours with no significant volume differences detected, but SCULPTER delineations were significantly quicker (p<0.05) in most cases. In conclusion, clinical application of SCULPTER resulted in rapid and simple organ delineations with equivalent accuracy to manual methods, demonstrating proof-of-principle of the SCULPTER system and supporting its potential utility in RTP.     

The use of active shape models for making thickness measurements of articular cartilage from MR images

Previously reported studies to quantify articular cartilage have used labor-intensive manual or semi-automatic data-driven techniques, demonstrating high accuracy and precision. However, none has been able to automate the segmentation process. This paper describes a fast, automatic, model-based approach to segmentation and thickness measurement of the femoral cartilage in 3D T₁-weighted images using active shape models (ASMs). Systematic experiments were performed to assess the accuracy and precision of the technique with in vivo images of both normal and abnormal knees. Segmentation accuracy was determined by comparing the results of the segmentation with the boundaries delineated by a radiologist. The mean error in locating the boundary was 0.57 pixels. To assess the precision of the measurement technique, the mean thickness of the femoral cartilage was calculated for repeated scans of five healthy volunteers. A mean coefficient of variation (CV) of 2.8% was obtained for the thickness measurements.     

Asymmetry analysis in rodent cerebral ischemia models

RATIONALE AND OBJECTIVES: An automated method for identification and segmentation of acute/subacute ischemic stroke, using the inherent bi-fold symmetry in brain images, is presented. An accurate and automated method for localization of acute ischemic stroke could provide physicians with a mechanism for early detection and potentially faster delivery of effective stroke therapy. MATERIALS AND METHODS: Segmentation of ischemic stroke was performed on magnetic resonance (MR) images of subacute rodent cerebral ischemia. Eight adult male Wistar rats weighing 225-300 g were anesthetized with halothane in a mix of 70% nitrous oxide/30% oxygen. Animal core temperature was maintained at 37 degrees C during the entire surgical procedure, including occlusion of the middle cerebral artery (MCA) and the 90-minute post-reperfusion period. To confirm cerebral ischemia, transcranial measurements of cerebral blood flow were performed with laser-Doppler flowmetry, using 15-mm flexible fiberoptic Doppler probes attached to the skull over the MCA territory. Animal MR scans were performed at 1.5 T using a knee coil. Three experts performed manual tracing of the stroke regions for each rat, using the histologic-stained slices to guide delineation of stroke regions. A strict tracing protocol was followed that included multiple (three) tracings of each stroke region. The volumetric MR image data were processed for each rat by computing the axis of symmetry and extracting statistical dissimilarities. A nonparametric Wilcoxon rank sum test operating on paired windows in opposing hemispheres identified seeds in the pixels exhibiting statistically significant bi-fold mirror asymmetry. Two brain reference maps were used for analysis: an absolute difference map (ADM) and a statistical difference map (SDM). Although an ADM simply displays the absolute difference by subtracting one brain hemisphere from its reflection, SDM highlights regions by labeling pixels exhibiting statistically significant asymmetry. RESULTS: To assess the accuracy of the proposed segmentation method, the surrogate ground truth (the stroke tracing data) was compared to the results of our proposed automated segmentation algorithm. Three accuracy segmentation metrics were utilized: true-positive volume fraction (TPVF), false-positive volume fraction (FPVF), and false-negative volume fraction (FNVF). The mean value of the TPVF for our segmentation method was 0.8877; 95% CI 0.7254 to 1.0500; the mean FPVF was 0.3370, 95% CI -0.0893 to 0.7633; the mean FNVF was 0.1122, 95% CI -0.0502 to 0.2747. CONCLUSIONS: Unlike most segmentation methods that require some degree of manual intervention, our segmentation algorithm is fully automated and highly accurate in identifying regions of brain asymmetry. This approach is attractive for numerous neurologic applications where the operator's intervention should be minimal or null.     

A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images

RATIONALE AND OBJECTIVES: Accurate quantification of the shape and extent of breast tumors has a vital role in nearly all applications of breast magnetic resonance (MR) imaging (MRI). Specifically, tumor segmentation is a key component in the computerized assessment of likelihood of malignancy. However, manual delineation of lesions in four-dimensional MR images is labor intensive and subject to interobserver and intraobserver variations. We developed a computerized lesion segmentation method that has the advantage of being automatic, efficient, and objective. MATERIALS AND METHODS: We present a fuzzy c-means (FCM) clustering-based method for the segmentation of breast lesions in three dimensions from contrast-enhanced MR images. The proposed lesion segmentation algorithm consists of six consecutive stages: region of interest (ROI) selection by a human operator, lesion enhancement within the selected ROI, application of FCM on the enhanced ROI, binarization of the lesion membership map, connected-component labeling and object selection, and hole-filling on the selected object. We applied the algorithm to a clinical MR database consisting of 121 primary mass lesions. Manual segmentation of the lesions by an expert MR radiologist served as a reference in the evaluation of the computerized segmentation method. We also compared the proposed algorithm with a previously developed volume-growing (VG) method. RESULTS: For the 121 mass lesions in our database, 97% of lesions were segmented correctly by means of the proposed FCM-based method at an overlap threshold of 0.4, whereas 84% of lesions were correctly segmented by means of the VG method. CONCLUSION: Our proposed algorithm for breast-lesion segmentation in dynamic contrast-enhanced MRI was shown to be effective and efficient.     

Segmentation propagation using a 3D embryo atlas for high‐throughput MRI phenotyping: Comparison and validation with manual segmentation

Effective methods for high‐throughput screening and morphometric analysis are crucial for phenotyping the increasing number of mouse mutants that are being generated. Automated segmentation propagation for embryo phenotyping is an emerging application that enables noninvasive and rapid quantification of substructure volumetric data for morphometric analysis. We present a study to assess and validate the accuracy of brain and kidney volumes generated via segmentation propagation in an ex vivo mouse embryo MRI atlas comprising three different groups against the current “gold standard”—manual segmentation. Morphometric assessment showed good agreement between automatically and manually segmented volumes, demonstrating that it is possible to assess volumes for phenotyping a population of embryos using segmentation propagation with the same variation as manual segmentation. As part of this study, we have made our average atlas and segmented volumes freely available to the community for use in mouse embryo phenotyping studies. These MRI datasets and automated methods of analyses will be essential for meeting the challenge of high‐throughput, automated embryo phenotyping. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.     

Pathology-based validation of FDG PET segmentation tools for volume assessment of lymph node metastases from head and neck cancer

Purpose

FDG PET is increasingly incorporated into radiation treatment planning of head and neck cancer. However, there are only limited data on the accuracy of radiotherapy target volume delineation by FDG PET. The purpose of this study was to validate FDG PET segmentation tools for volume assessment of lymph node metastases from head and neck cancer against the pathological method as the standard.

Methods

Twelve patients with head and neck cancer and 28 metastatic lymph nodes eligible for therapeutic neck dissection underwent preoperative FDG PET/CT. The metastatic lymph nodes were delineated on CT (Node_CT) and ten PET segmentation tools were used to assess FDG PET-based nodal volumes: interpreting FDG PET visually (PET_VIS), applying an isocontour at a standardized uptake value (SUV) of 2.5 (PET_SUV), two segmentation tools with a fixed threshold of 40 % and 50 %, and two adaptive threshold based methods. The latter four tools were applied with the primary tumour as reference and also with the lymph node itself as reference. Nodal volumes were compared with the true volume as determined by pathological examination.

Results

Both Node_CT and PET_VIS showed good correlations with the pathological volume. PET segmentation tools using the metastatic node as reference all performed well but not better than PET_VIS. The tools using the primary tumour as reference correlated poorly with pathology. PET_SUV was unsatisfactory in 35 % of the patients due to merging of the contours of adjacent nodes.

Conclusion

FDG PET accurately estimates metastatic lymph node volume, but beyond the detection of lymph node metastases (staging), it has no added value over CT alone for the delineation of routine radiotherapy target volumes. If FDG PET is used in radiotherapy planning, treatment adaptation or response assessment, we recommend an automated segmentation method for purposes of reproducibility and interinstitutional comparison.     

基于新过渡区提取法的医学图像交互式分割

目的：为了提高医学图像三维重组过程中二维图像交互分割的效率,我们试验了一种新过渡区提取方法,并验证了其分割效率和分割效果。方法：新过渡区提取法先依据原始图像,分散构造过渡片段,后将过渡片段按一定规则连接成过渡区,在过渡区的基础上实现交互分割。以512×512的CT图像序列为样本进行实验,结果与基于边缘提取的交互分割法及完全手工分割法比较。结果：基于新过渡区提取的交互分割法对目标区域的分割精确度高于边缘提取分割法,更接近完全手工分割,而人工操作时间较基于边缘提取的分割法及完全手工分割法分别节省39%和62%。结论：新过渡区提取法保留了过渡区分割法抗干扰能力强、对弱边界提取效果好的优点。应用于交互分割,能够减少人工劳动,提高分割效率,同时分割精确度高于边缘提取法,重组后能更准确反映目标特征。     

Classification and segmentation of intracardiac masses in cardiac tumor echocardiograms.

This paper describes an automatic method for classification and segmentation of different intracardiac masses in tumor echocardiograms. Identification of mass type is highly desirable, since to different treatment options for cardiac tumors (surgical resection) and thrombi (effective anticoagulant treatment) are possible. Correct diagnosis of the character of intracardiac mass in a living patient is a true challenge for a cardiologist; therefore, an objective image analysis method may be useful in heart diseases diagnosis. Image texture analysis is used to distinguish various types of masses. The presented methods assume that image texture encodes important histological features of masses and, therefore, texture numerical parameters enable the discrimination and segmentation of a mass. The recently developed technique based on the network of synchronized oscillators is proposed for the image segmentation. This technique is based on a 'temporary correlation' theory, which attempts to explain scene recognition as it would be performed by a human brain. This theory assumes that different groups of neural cells encode different properties of homogeneous image regions (e.g. shape, color, texture). Monitoring of temporal activity of cell groups leads to scene segmentation. A network of synchronized oscillators was successfully used for segmentation of Brodatz textures and medical textured images. The advantage of this network is its ability to detect texture boundaries. It can be also manufactured as a VLSI chip, for a very fast image segmentation. The accuracy of locating of analyzed tissues in the image should be assessed to evaluate a segmentation technique. The new evaluation method based on measurement of physical textured test objects was proposed. Firstly, a series of object images was obtained by the use of different devices (scanner, digital camera and TV camera). Secondly, the images were segmented using oscillator network and feedforward artificial neural network. Thirdly, geometrical test object parameters were estimated and compared to its true values. The experiment was repeated also for ultrasound images, which represented rectangular cross-section of synthetic sponge submerged in water. In addition, classification and segmentation of selected benign tumor echocardiograms were performed. Oscillator network was used with network weights defined for both whole texture region and texture boundary detection for the tumor segmentation. The latter method provides much faster segmentation with the similar accuracy. The obtained segmentation results were discussed and compared to the artificial neural network classifier. Finally, it was demonstrated that the network of synchronized oscillators is a reliable tool for the segmentation of the selected intracardiac masses, since it gives a relatively accurate location of analyzed tissues. The advantage of the proposed method is its resistance to changes of the visual information in the analyzed image and to noise and artifacts, often present in echocardiograms.     

Cardiac segmentation by a velocity-aided active contour model.

Heart disease is one of the more life-threatening diseases. Accurate diagnosis and treatment are central to the survival of patients. Numerous diagnostic methods that can assess abnormalities of the heart have been developed. Among these methods, cardiac functional analysis has been widely used to derive cardiac functional parameters that describe the functionality of the heart and are frequently used in diagnosis of various heart diseases. Segmentation of the myocardial boundaries is an essential step for deriving these cardiac functional parameters, and the accuracy of parameters depends much on the correctness of the segmented boundaries. Therefore, it is essential that cardiac segmentation be accurate and reliable. However, current segmentation techniques still have difficulty both extracting accurate myocardial boundaries, especially the endocardial boundary and performing a fully automatic process because of low image quality, the complex shape and motion pattern of the heart, and lack of clear delineation between the myocardium and adjacent anatomic structures. A velocity-aided cardiac segmentation method based a modified active contour model, the tensor-based orientation gradient force (OGF) and phase contrast magnetic resonance imaging (MRI) has been developed to improve the accuracy of segmentation of the myocardial boundaries, especially the endocardial boundary. Furthermore, the initial seed contour tracking (SCT) algorithm has been also developed to improve the accuracy of automatic sequential frame segmentation in conjunction with the OGF-based segmentation method. The performance of the proposed method was assessed by experimentations on a phase contrast MRI data set of three normal human volunteer. The results of the individual frame segmentation showed that the accuracy and reproducibility of segmentation of the endocardial boundary by the use of the OGF was generally improved around the lower level of the LV and end systole. The results of the sequential frame segmentation showed that the propagation of errors caused was significantly reduced by the use of the SCT in addition to the OGF and improvements in the accuracy and reproducibility of segmentation of the endocardial boundary were much higher than the individual frame segmentation. However, improvements were generally negligible around the upper level of the LV and end diastole, and the velocity wrap-around problem and blood turbulence around the basal level of the ventricles even degraded the performance of boundary segmentation. Although this work demonstrates the potential of using the velocity information from phase contrast MRI for cardiac segmentation, the velocity wrap-around artifacts in phase contrast MRI data sets can degrade the performance. Therefore, future work must include the development of appropriate methods to cope with these artifacts.