CNN-based Landmark Detection in Cardiac CTA Scans

Fast and accurate anatomical landmark detection can benefit many medical image analysis methods. Here, we propose a method to automatically detect anatomical landmarks in medical images. Automatic landmark detection is performed with a patch-based fully convolutional neural network (FCNN) that combines regression and classification. For any given image patch, regression is used to predict the 3D displacement vector from the image patch to the landmark. Simultaneously, classification is used to identify patches that contain the landmark. Under the assumption that patches close to a landmark can determine the landmark location more precisely than patches farther from it, only those patches that contain the landmark according to classification are used to determine the landmark location. The landmark location is obtained by calculating the average landmark location using the computed 3D displacement vectors. The method is evaluated using detection of six clinically relevant landmarks in coronary CT angiography (CCTA) scans: the right and left ostium, the bifurcation of the left main coronary artery (LM) into the left anterior descending and the left circumflex artery, and the origin of the right, non-coronary, and left aortic valve commissure. The proposed method achieved an average Euclidean distance error of 2.19 mm and 2.88 mm for the right and left ostium respectively, 3.78 mm for the bifurcation of the LM, and 1.82 mm, 2.10 mm and 1.89 mm for the origin of the right, non-coronary, and left aortic valve commissure respectively, demonstrating accurate performance. The proposed combination of regression and classification can be used to accurately detect landmarks in CCTA scans.Comment: This work was submitted to MIDL 2018 Conferenc

Database guided detection of anatomical landmark points in 3D images of the heart

ABSTRACT Automated landmark detection may prove invaluable in the analysis of real-time three-dimensional (3D) echocardiograms. By detecting 3D anatomical landmark points, the standard anatomical views can be extracted automatically in apically acquired 3D ultrasound images of the left ventricle, for better standardization of visualization and objective diagnosis. Furthermore, the landmarks can serve as an initialization for other analysis methods, such as segmentation. The described algorithm applies landmark detection in perpendicular planes of the 3D dataset. The landmark detection exploits a large database of expert annotated images, using an extensive set of Haar features for fast classification. The detection is performed using two cascades of Adaboost classifiers in a coarse to fine scheme. The method is evaluated by measuring the distance of detected and manually indicated landmark points in 25 patients. The method can detect landmarks accurately in the four-chamber (apex: 7.9±7.1mm, septal mitral valve point: 5.6±2.7mm; lateral mitral valve point: 4.0±2.6mm) and two-chamber view (apex: 7.1±6.7mm, anterior mitral valve point: 5.8±3.5mm, inferior mitral valve point: 4.5±3.1mm). The results compare well to those reported by others

Database guided detection of anatomical landmark points in 3D images of the heart

Information and Communication Theory GroupElectrical Engineering, Mathematics and Computer Scienc

Database guided detection of anatomical landmark points in 3D images of the heart

Automated landmark detection may prove important for the examination and automatic analysis of real-time three-dimensional (3D) echocardiograms. By detecting 3D anatomical landmark points, the standard anatomical views can be extracted automatically in 3D ultrasound images of left ventricle, for better standardization and objective diagnosis. Furthermore, the landmarks can serve as an initialization for other analysis methods, such as segmentation. In this thesis we describe an algorithm that iteratively applies landmark detection in perpendicular planes of the 3D dataset. The landmark detection exploits a large database of expert annotated images, using an extensive set of Haar wavelet-like features for classification, resulting in fast detection times suitable for real-time applications. The detection is performed using two cascades of Adaboost classifiers, that work in different 2D planes, in a coarse to fine scheme. The method is evaluated by measuring the total detection error for the landmark points between the detected positions and the manual ones.Media and Knowledge EngineeringInformation and Communication Theory GroupElectrical Engineering, Mathematics and Computer Scienc