CNN-based Landmark Detection in Cardiac CTA Scans
Abstract: 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.
Keywords: Landmark Detection, Convolutional Neural Network, Regression, Classification, CCTA
Author Affiliation: Image Sciences Institute, University Medical Center Utrecht and Utrecht University, The Netherlands
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