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2018 | OriginalPaper | Buchkapitel

Automatic Shadow Detection in 2D Ultrasound Images

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

Erschienen in: Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

Verlag: Springer International Publishing

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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 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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Metadaten
Titel
Automatic Shadow Detection in 2D Ultrasound Images
verfasst von
Qingjie Meng
Christian Baumgartner
Matthew Sinclair
James Housden
Martin Rajchl
Alberto Gomez
Benjamin Hou
Nicolas Toussaint
Veronika Zimmer
Jeremy Tan
Jacqueline Matthew
Daniel Rueckert
Julia Schnabel
Bernhard Kainz
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-00807-9_7