STACS: new active contour scheme for cardiac MR image segmentation

Abstract

Abstract-The paper presents a novel stochastic active contour scheme (STACS) for automatic image segmentation designed to overcome some of the unique challenges in cardiac MR images such as problems with low contrast, papillary muscles, and turbulent blood flow. STACS minimizes an energy functional that combines stochastic region-based and edge-based information with shape priors of the heart and local properties of the contour. The minimization algorithm solves, by the level set method, the Euler-Lagrange equation that describes the contour evolution. STACS includes an annealing schedule that balances dynamically the weight of the different terms in the energy functional. Three particularly attractive features of STACS are: 1) ability to segment images with low texture contrast by modeling stochastically the image textures; 2) robustness to initial contour and noise because of the utilization of both edge and region-based information; 3) ability to segment the heart from the chest wall and the undesired papillary muscles due to inclusion of heart shape priors. Application of STACS to a set of 48 real cardiac MR images shows that it can successfully segment the heart from its surroundings such as the chest wall and the heart structures (the left and right ventricles and the epicardium.) We compare STACS' automatically generated contours with manually-traced contours, or the "gold standard," using both area and edge similarity measures. This assessment demonstrates very good and consistent segmentation performance of STACS.Index Terms-Active contour, cardiac magnetic resonance imaging (cardiac MRI), chamfer method, energy minimization, image segmentation, level set, shape and area similarities, stochastic model, stochastic relaxation.

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

“…The higher the DM, the better is the segmentation. Generally, DM values higher than 0.80 indicate good segmentation for cardiac images [34].…”

“…To quantify the segmentation accuracy of our method, we use the dice metric (DM) [34] and HD. DM measures the overlap between the segmentation obtained by our algorithm and reference manual segmentation and is given by…”

“…Some of the works on LV segmentation also show results for RV segmentation. These include image-based approaches like thresholding [28][29][30], pixel classification approaches [31,32], deformable models [33][34][35][36], active appearance models [27,37], and atlas based methods [19,23,38].…”

Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

“…When the initial DM ≤62, the final segmentations have DM <80. In [27], the authors report that DM ≥80 indicates good agreement with manual segmentations for cardiac images. Table 4 summarizes the final segmentation results for different degrees of undersegmentation.…”

“…AAM and ASM (active shape models) were combined in [25], with extensions to the time domain in [26]. Some of the works on LV segmentation also show results for RV segmentation using deformable models [27,28] and atlasbased methods [29]. However, to our knowledge, none of the works use any kind of contextual information from the RV (or LV) to segment the LV (or RV).…”