Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes

Abstract

Multi-chamber heart segmentation is a prerequisite for quantification of the cardiac function. In this paper, we propose an automatic heart chamber segmentation system. There are two closely related tasks to develop such a system: heart modeling and automatic model fitting to an unseen volume. The heart is a complicated non-rigid organ with four chambers and several major vessel trunks attached. A flexible and accurate model is necessary to capture the heart chamber shape at an appropriate level of details. In our four-chamber surface mesh model, the following two factors are considered and traded-off: 1) accuracy in anatomy and 2) easiness for both annotation and automatic detection. Important landmarks such as valves and cusp points on the interventricular septum are explicitly represented in our model. These landmarks can be detected reliably to guide the automatic model fitting process. We also propose two mechanisms, the rotation-axis based and parallel-slice based resampling methods, to establish mesh point correspondence, which is necessary to build a statistical shape model to enforce priori shape constraints in the model fitting procedure.Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3D computed tomography (CT) volumes. Our approach is based on recent advances in learning discriminative object models and we exploit a large database of annotated CT volumes. We formulate the segmentation as a twostep learning problem: anatomical structure localization and boundary delineation. A novel algorithm, Marginal Space Learning (MSL), is introduced to solve the 9-dimensional similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3D shape through learning-based boundary delineation. Extensive experiments demonstrate the efficiency and robustness of the proposed approach, comparing favorably to the state-of-the-art. This is the first study reporting stable results on a large cardiac CT dataset with 323 volumes. In addition, we achieve a speed of less than eight seconds for automatic segmentation of all four chambers.

A framework for the merging of pre-existing and correspondenceless 3D statistical shape models

“…(Davatzikos et al, 2003;Okada et al, 2007Okada et al, , 2008Ecabert et al, 2008;Zheng et al, 2008;Lekadir et al, 2011). On a similar note, promoting sparsity in the statistical decomposition can provide shape models that are easier to manipulate as its modes of variation tend to be local rather than global (Üzümcü et al, 2003;Stegmann et al, 2006;Loog, 2007;Sjöstrand et al, 2007).…”

Challenges and Methodologies of Fully Automatic Whole Heart Segmentation: A Review

“…The goal of whole heart segmentation is to extract the volume and shapes of all the substructures of the heart, commonly including the blood cavities of the four chambers, left ventricle myocardium, and sometimes the great vessels as well if they are of interest [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. This is in contrast with many other cardiac segmentation works in the literature, which focus solely on the ventricles and/or left ventricle myocardium [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44], or isolation of the whole heart volume [45][46][47].…”

“…Automatic segmentation of complex organs, such as the heart, is generally formulated as a fitting process from a prior model to the unseen images for segmentation propagation [9][10][11][12][13][14][15][16][17][18][19][20][21][22]. The prior models contain the segmentation information and are used to guide the segmentation procedure, and the fitting procedure is generally implemented in a hierarchy manner.…”

“…Images reproduced with permission [23]. and the deformable model [11][12][13][14][15]68]. In the atlas-based segmentation, one can construct an atlas pool, which contains atlases with different heart shapes, and use a multiple classification strategy to improve the segmentation [17,24,69,70].…”

“…Fitting process: The fitting process requires a global localization to perform a deformable fitting. To improve the robustness, several studies proposed to use the hierarchical scheme, where the deformable fitting can be further decomposed into two stages: the initialization of substructures and the refinement of boundary delineation in detail [11][12][13][14][15]19]. In the deformable fitting, shape regularization is required to maintain a realistic and smooth heart shape in the resultant segmentation.…”