TY - CHAP
T1 - CAS-Net
T2 - 3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
AU - Li, Liu
AU - Sinclair, Matthew
AU - Makropoulos, Antonios
AU - Hajnal, Joseph V.
AU - David Edwards, A.
AU - Kainz, Bernhard
AU - Rueckert, Daniel
AU - Alansary, Amir
N1 - Funding Information:
Data in this work were provided by ERC Grant Agreement no. [319456]. We are grateful to the families who generously supported this trial.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the correctness of both manual and automatic segmentation techniques. In this paper, we propose a novel network structure that can simultaneously generate conditional atlases and predict brain tissue segmentation, called CAS-Net. The conditional atlases provide anatomical priors that can constrain the segmentation connectivity, despite the heterogeneity of intensity values caused by motion or partial volume effects. The proposed method is trained and evaluated on 253 subjects from the developing Human Connectome Project (dHCP). The results demonstrate that the proposed method can generate conditional age-specific atlas with sharp boundary and shape variance. It also segment multi-category brain tissues for fetal MRI with a high overall Dice similarity coefficient (DSC) of 85.2 % for the selected 9 tissue labels.
AB - Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the correctness of both manual and automatic segmentation techniques. In this paper, we propose a novel network structure that can simultaneously generate conditional atlases and predict brain tissue segmentation, called CAS-Net. The conditional atlases provide anatomical priors that can constrain the segmentation connectivity, despite the heterogeneity of intensity values caused by motion or partial volume effects. The proposed method is trained and evaluated on 253 subjects from the developing Human Connectome Project (dHCP). The results demonstrate that the proposed method can generate conditional age-specific atlas with sharp boundary and shape variance. It also segment multi-category brain tissues for fetal MRI with a high overall Dice similarity coefficient (DSC) of 85.2 % for the selected 9 tissue labels.
UR - http://www.scopus.com/inward/record.url?scp=85117067865&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87735-4_21
DO - 10.1007/978-3-030-87735-4_21
M3 - Conference paper
AN - SCOPUS:85117067865
SN - 9783030877347
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 221
EP - 230
BT - Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis - 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Proceedings
A2 - Sudre, Carole H.
A2 - Licandro, Roxane
A2 - Baumgartner, Christian
A2 - Melbourne, Andrew
A2 - Dalca, Adrian
A2 - Hutter, Jana
A2 - Tanno, Ryutaro
A2 - Abaci Turk, Esra
A2 - Van Leemput, Koen
A2 - Torrents Barrena, Jordina
A2 - Wells, William M.
A2 - Macgowan, Christopher
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 October 2021 through 1 October 2021
ER -