Multi-atlas Joint Label Fusion Research Articles

Structured graph regularized shape prior and cross-entropy induced active contour model for myocardium segmentation in CTA images

Accurate segmentation of myocardium tissue from computed tomography angiography (CTA) images is a crucial step in development of cardiac clinical applications. Automatic methods are highly desirable to release the burden of manual delineation, however, it is challenging due to the presence of images with intensity non-uniformity and coarse, weak and even pseudo boundary. To this end, a new geometric active contour (GAC) model that integrates high-level shape prior and low-level local intensity statistics is proposed. First, the cardiac-type specific shape prior is learned by structured graph-regularized principal component analysis, so that allows to be faithful to the shape of the desired myocardium. Second, local intensity distribution with inhomogeneity is modeled by cross-entropy energy functional and segmentation-oriented image decomposition. Third, the proposed model is solved as variational level set formulation and a distance regularized energy functional is also incorporated for the stability of numerical computation. The model was evaluated on a set of cardiac CTA images with comparison to related shape prior and local region-based methods and multi-atlas joint label fusion methods, and experimental results show it achieves competitive accuracies of segmenting myocardial epicardium and endocardium parts. It also presents advantage of computational burden in comparison to some popular methods such as multi-atlas joint label fusion. Since the framework of proposed method is general and adaptive, it could be potentially extended to segment similar objects in CT images.

Automated segmentation of the thyroid gland on thoracic CT scans by multiatlas label fusion and random forest classification.

The thyroid is an endocrine gland that regulates metabolism. Thyroid image analysis plays an important role in both diagnostic radiology and radiation oncology treatment planning. Low tissue contrast of the thyroid relative to surrounding anatomic structures makes manual segmentation of this organ challenging. This work proposes a fully automated system for thyroid segmentation on CT imaging. Following initial thyroid segmentation with multiatlas joint label fusion, a random forest (RF) algorithm was applied. Multiatlas label fusion transfers labels from labeled atlases and warps them to target images using deformable registration. A consensus atlas solution was formed based on optimal weighting of atlases and similarity to a given target image. Following the initial segmentation, a trained RF classifier employed voxel scanning to assign class-conditional probabilities to the voxels in the target image. Thyroid voxels were categorized with positive labels and nonthyroid voxels were categorized with negative labels. Our method was evaluated on CT scans from 66 patients, 6 of which served as atlases for multiatlas label fusion. The system with independent multiatlas label fusion method and RF classifier achieved average dice similarity coefficients of [Formula: see text] and [Formula: see text], respectively. The system with sequential multiatlas label fusion followed by RF correction increased the dice similarity coefficient to [Formula: see text] and improved the segmentation accuracy.

Fully automatic segmentation of the mitral leaflets in 3D transesophageal echocardiographic images using multi-atlas joint label fusion and deformable medial modeling.

Comprehensive visual and quantitative analysis of in vivo human mitral valve morphology is central to the diagnosis and surgical treatment of mitral valve disease. Real-time 3D transesophageal echocardiography (3D TEE) is a practical, highly informative imaging modality for examining the mitral valve in a clinical setting. To facilitate visual and quantitative 3D TEE image analysis, we describe a fully automated method for segmenting the mitral leaflets in 3D TEE image data. The algorithm integrates complementary probabilistic segmentation and shape modeling techniques (multi-atlas joint label fusion and deformable modeling with continuous medial representation) to automatically generate 3D geometric models of the mitral leaflets from 3D TEE image data. These models are unique in that they establish a shape-based coordinate system on the valves of different subjects and represent the leaflets volumetrically, as structures with locally varying thickness. In this work, expert image analysis is the gold standard for evaluating automatic segmentation. Without any user interaction, we demonstrate that the automatic segmentation method accurately captures patient-specific leaflet geometry at both systole and diastole in 3D TEE data acquired from a mixed population of subjects with normal valve morphology and mitral valve disease.

Groupwise segmentation with multi-atlas joint label fusion.

Groupwise segmentation that simultaneously segments a set of images and ensures that the segmentations for the same structure of interest from different images are consistent usually can achieve better performance than segmenting each image independently. Our main contribution is that we adopt the groupwise segmentation framework to improve the performance of multi-atlas label fusion. We develop a novel statistical model to allow this extension. Comparing to previous atlas propagation and groupwise segmentation work, one key novelty of our method is that the error produced during label propagation is explicitly addressed in the joint label fusion framework. Experiments on hippocampus segmentation in magnetic resonance images show the effectiveness of the new groupwise segmentation technique.