Automatic Segmentation of the Left Ventricle in Cardiac CT Angiography Using Convolutional Neural Network

Accurate delineation of the left ventricle (LV) is an important step in evaluation of cardiac function. In this paper, we present an automatic method for segmentation of the LV in cardiac CT angiography (CCTA) scans. Segmentation is performed in two stages. First, a bounding box around the LV is detected using a combination of three convolutional neural networks (CNNs). Subsequently, to obtain the segmentation of the LV, voxel classification is performed within the defined bounding box using a CNN. The study included CCTA scans of sixty patients, fifty scans were used to train the CNNs for the LV localization, five scans were used to train LV segmentation and the remaining five scans were used for testing the method. Automatic segmentation resulted in the average Dice coefficient of 0.85 and mean absolute surface distance of 1.1 mm. The results demonstrate that automatic segmentation of the LV in CCTA scans using voxel classification with convolutional neural networks is feasible.Comment: This work has been published as: Zreik, M., Leiner, T., de Vos, B. D., van Hamersvelt, R. W., Viergever, M. A., I\v{s}gum, I. (2016, April). Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 40-43). IEE

3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation

We present a novel automated method to segment the myocardium of both left and right ventricles in MRI volumes. The segmentation is consistent in 3D across the slices such that it can be directly used for mesh generation. Two specific neural networks with multi-scale coarse-to-fine prediction structure are proposed to cope with the small training dataset and trained using an original loss function. The former segments a slice in the middle of the volume. Then the latter iteratively propagates the slice segmentations towards the base and the apex, in a spatially consistent way. We perform 5-fold cross-validation on the 15 cases from STACOM to validate the method. For training, we use real cases and their synthetic variants generated by combining motion simulation and image synthesis. Accurate and consistent testing results are obtained

Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structures

A spatial geometric plane is defined by the three-dimensional coordinates of a pair of spatial points and the direction that the normal vector establishes, which is formed by joining those points by means of an oriented line segment. This type of planes, in three-dimensional images, is extremely useful as an alternative solution to the problem of low contrast that exhibit the anatomical structures present in cardiac computed tomography images. To do this, after using a predetermined filter bank and in order to define a region of interest, a smart operator based on least squares support vector machines is trained and validated in order to detect the aforementioned coordinates which enables the location of the plane, in the three-dimensional space that contains the considered images. Once the structure that is required to segment is identified, a discriminant function is used that cancels all information not linked to this structure. In this work, the segmentation of the left ventricle, based on region growing technique, is firstly considered and then the left atrium is segmented considering region growing technique and an inverse discriminant function. The results show an excellent correspondence relationship when the spatial union of both structures is made