TY - GEN
T1 - Towards whole placenta segmentation at late gestation using multi-view ultrasound images
AU - Zimmer, Veronika Anne Maria
AU - Gomez Herrero, Alberto
AU - Skelton, Emily
AU - Toussaint, Nicolas Alexis
AU - Zhang, Tong
AU - Khanal, Bishesh
AU - Wright, Robert
AU - Noh, Yohan
AU - Ho, Alison Elizabeth Puiyun
AU - Matthew, Jacqueline
AU - Hajnal, Joseph Vilmos
AU - Schnabel, Julia Anne
PY - 2019/10/10
Y1 - 2019/10/10
N2 - We propose a method to extract the human placenta at late gestation using multi-view 3D US images. This is the first step towards automatic quantification of placental volume and morphology from US images along the whole pregnancy beyond early stages (where the entire placenta can be captured with a single 3D US image). Our method uses 3D US images from different views acquired with a multi-probe system. A whole placenta segmentation is obtained from these images by using a novel technique based on 3D convolutional neural networks. We demonstrate the performance of our method on 3D US images of the placenta in the last trimester. We achieve a high Dice overlap of up to 0.8 with respect to manual annotations, and the derived placental volumes are comparable to corresponding volumes extracted from MR.
AB - We propose a method to extract the human placenta at late gestation using multi-view 3D US images. This is the first step towards automatic quantification of placental volume and morphology from US images along the whole pregnancy beyond early stages (where the entire placenta can be captured with a single 3D US image). Our method uses 3D US images from different views acquired with a multi-probe system. A whole placenta segmentation is obtained from these images by using a novel technique based on 3D convolutional neural networks. We demonstrate the performance of our method on 3D US images of the placenta in the last trimester. We achieve a high Dice overlap of up to 0.8 with respect to manual annotations, and the derived placental volumes are comparable to corresponding volumes extracted from MR.
UR - http://www.scopus.com/inward/record.url?scp=85075655653&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32254-0_70
DO - 10.1007/978-3-030-32254-0_70
M3 - Conference contribution
SN - 9783030322533
VL - 11768
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 628
EP - 636
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
ER -