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Meng, Q.* ; Baumgartner, C.* ; Sinclair, M.* ; Housden, J.* ; Rajchl, M.* ; Gomez, A.* ; Hou, B.* ; Toussaint, N.* ; Zimmer, V.* ; Tan, J.* ; Matthew, J.* ; Rueckert, D.* ; Schnabel, J.A.* ; Kainz, B.*

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)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Workshop on Preterm, Perinatal and Paediatric Image Analysis
Quellenangaben Band: 11076 LNCS, Heft: , Seiten: 66-75 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)