Automatic Shadow Detection in 2D Ultrasound

Qingjie Meng, Christian Baumgartner, Matthew Sinclair, James Housden, Martin Rajchl, Alberto Gomez, Benjamin Hou, Nicolas Toussaint, Jeremy Tan, Jacqueline Matthew, Daniel Rueckert, Julia Schnabel, Bernhard Kainz

Abstract: Automatically detecting acoustic shadows is of great importance for automatic 2D ultrasound analysis ranging from anatomy segmentation to landmark detection. However, variation in shape and similarity in intensity to other structures in the image make shadow detection a very challenging task. In this paper, we propose an automatic shadow detection method to generate a pixel-wise shadow confidence map from weakly labelled annotations. Our method jointly uses; (1) a feature attribution map from a Wasserstein GAN and (2) an intensity saliency map from a graph cut model. The proposed method accurately highlights the shadow areas in two 2D ultrasound datasets comprising standard view planes as acquired during fetal screening. Moreover, the proposed method outperforms the state-of-the-art quantitatively and improves failure cases for automatic biometric measurement.