TY - CHAP
T1 - Automatic shadow detection in 2D ultrasound images
AU - Meng, Qingjie
AU - Baumgartner, Christian
AU - Sinclair, Matthew
AU - Housden, James
AU - Rajchl, Martin
AU - Gomez, Alberto
AU - Hou, Benjamin
AU - Toussaint, Nicolas
AU - Zimmer, Veronika
AU - Tan, Jeremy
AU - Matthew, Jacqueline
AU - Rueckert, Daniel
AU - Schnabel, Julia
AU - Kainz, Bernhard
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85054866052&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00807-9_7
DO - 10.1007/978-3-030-00807-9_7
M3 - Conference paper
AN - SCOPUS:85054866052
SN - 9783030008062
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 66
EP - 75
BT - Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis - First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018 Held in Conjunction with MICCAI 2018, Proceedings
PB - Springer Verlag
T2 - 1st International Workshop on Data Driven Treatment Response Assessment, DATRA 2018 and 3rd International Workshop on Preterm, Perinatal, and Paediatric Image Analysis, PIPPI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 16 September 2018
ER -