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Open Access Green as soon as Postprint is submitted to ZB.
Automatic shadow detection in 2D ultrasound images.
In: (International Workshop on Preterm, Perinatal and Paediatric Image Analysis). Berlin [u.a.]: Springer, 2018. 66-75 (Lect. Notes Comput. Sc. ; 11076 LNCS)
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 make shadow detection a very challenging task. In this paper, we propose an automatic shadow detection method to generate a pixel-wise, shadow-focused confidence map from weakly labelled, anatomically-focused images. Our method: (1) initializes potential shadow areas based on a classification task. (2) extends potential shadow areas using a GAN model. (3) adds intensity information to generate the final confidence map using a distance matrix. The proposed method accurately highlights the shadow areas in 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.
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Publication type
Article: Conference contribution
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
International Workshop on Preterm, Perinatal and Paediatric Image Analysis
Quellenangaben
Volume: 11076 LNCS,
Pages: 66-75
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)