A Cascade Regression Model for Anatomical Landmark Detection

Abstract

Automatic anatomical landmark detection is beneficial to many other medical image analysis tasks. In this paper, we propose a two-stage cascade regression model to make coarse-to-fine landmark detection. Specifically, in the first stage, a Gaussian heatmap regression model customized from U-Net is exploited to make primary prediction, which takes the downsampled entire image as input. In the second stage, we develop a CNN to regress displacements from the primary prediction to the landmarks, using patches in original resolution centered at the previous localization as input. Owing to the different sizes and resolutions of inputs in two stages, the global context information and local appearance can be integrated by our algorithm. The spacial relationships among landmarks can also be exploited by predicting all the landmarks simultaneously. In evaluation on the coronary and aorta CTA images, we show that our proposed method is widely applicable and delivers state-of-the-art performance even with limited training data.