Imperial College London
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DR BERNHARD KAINZ

Faculty of Engineering, Department of Computing

Reader in Medical Image Computing
 
 
 
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Contact

 

+44 (0)20 7594 8349b.kainz Website CV

 
 
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Location

 

372Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zimmer:2023:10.1016/j.media.2022.102639,
author = {Zimmer, VA and Gomez, A and Skelton, E and Wright, R and Wheeler, G and Deng, S and Ghavami, N and Lloyd, K and Matthew, J and Kainz, B and Rueckert, D and Hajnal, JV and Schnabel, JA},
doi = {10.1016/j.media.2022.102639},
journal = {Medical Image Analysis},
title = {Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view},
url = {http://dx.doi.org/10.1016/j.media.2022.102639},
volume = {83},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In this work, we address these three challenges with a multi-task learning approach that combines the classification of placental location (e.g., anterior, posterior) and semantic placenta segmentation in a single convolutional neural network. Through the classification task the model can learn from larger and more diverse datasets while improving the accuracy of the segmentation task in particular in limited training set conditions. With this approach we investigate the variability in annotations from multiple raters and show that our automatic segmentations (Dice of 0.86 for anterior and 0.83 for posterior placentas) achieve human-level performance as compared to intra- and inter-observer variability. Lastly, our approach can deliver whole placenta segmentation using a multi-view US acquisition pipeline consisting of three stages: multi-probe image acquisition, image fusion and image segmentation. This results in high quality segmentation of larger structures such as the placenta in US with reduced image artifacts which are beyond the field-of-view of single probes.
AU  - Zimmer,VA
AU  - Gomez,A
AU  - Skelton,E
AU  - Wright,R
AU  - Wheeler,G
AU  - Deng,S
AU  - Ghavami,N
AU  - Lloyd,K
AU  - Matthew,J
AU  - Kainz,B
AU  - Rueckert,D
AU  - Hajnal,JV
AU  - Schnabel,JA
DO  - 10.1016/j.media.2022.102639
PY  - 2023///
SN  - 1361-8415
TI  - Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view
T2  - Medical Image Analysis
UR  - http://dx.doi.org/10.1016/j.media.2022.102639
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36257132
UR  - http://hdl.handle.net/10044/1/100579
VL  - 83
ER  -
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