Cortical plate segmentation using CNNs in 3D fetal ultrasound
As the fetal brain develops, its surface undergoes rapid changes in shape and morphology. Variations in the emergence of the sulci on the brain surface have commonly been associated with diseased or at-risk pregnancies. Therefore, the process of surface folding is an important biomarker to character...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
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Springer
2020
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| _version_ | 1826313494097559552 |
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| author | Wyburd, MK Jenkinson, M Namburete, AIL |
| author_facet | Wyburd, MK Jenkinson, M Namburete, AIL |
| author_sort | Wyburd, MK |
| collection | OXFORD |
| description | As the fetal brain develops, its surface undergoes rapid
changes in shape and morphology. Variations in the emergence of the
sulci on the brain surface have commonly been associated with diseased or at-risk pregnancies. Therefore, the process of surface folding
is an important biomarker to characterise. Previous work has studied
such changes by automatically delineating the cortical plate from MRI
images. However, this has not been demonstrated from ultrasound, which
is more commonly used for antenatal care. In this work we propose
a novel method for segmenting the cortical plate from 3D ultrasound
images using three varieties of convolutional neural networks (CNNs).
Recent work has found improvements in medical image segmentations
using multi-task learning with a distance transform regularizer. Here we
implemented a similar method but found it was outperformed by the
U-Net, which was able to segment the cortical plate with a Dice score of
0.81 ± 0.06. |
| first_indexed | 2024-09-25T04:16:06Z |
| format | Conference item |
| id | oxford-uuid:cda2d02f-9ef4-48e4-a21d-ac1f7a7e396e |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:16:06Z |
| publishDate | 2020 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:cda2d02f-9ef4-48e4-a21d-ac1f7a7e396e2024-07-17T11:58:03ZCortical plate segmentation using CNNs in 3D fetal ultrasoundConference itemhttp://purl.org/coar/resource_type/c_5794uuid:cda2d02f-9ef4-48e4-a21d-ac1f7a7e396eEnglishSymplectic ElementsSpringer2020Wyburd, MKJenkinson, MNamburete, AILAs the fetal brain develops, its surface undergoes rapid changes in shape and morphology. Variations in the emergence of the sulci on the brain surface have commonly been associated with diseased or at-risk pregnancies. Therefore, the process of surface folding is an important biomarker to characterise. Previous work has studied such changes by automatically delineating the cortical plate from MRI images. However, this has not been demonstrated from ultrasound, which is more commonly used for antenatal care. In this work we propose a novel method for segmenting the cortical plate from 3D ultrasound images using three varieties of convolutional neural networks (CNNs). Recent work has found improvements in medical image segmentations using multi-task learning with a distance transform regularizer. Here we implemented a similar method but found it was outperformed by the U-Net, which was able to segment the cortical plate with a Dice score of 0.81 ± 0.06. |
| spellingShingle | Wyburd, MK Jenkinson, M Namburete, AIL Cortical plate segmentation using CNNs in 3D fetal ultrasound |
| title | Cortical plate segmentation using CNNs in 3D fetal ultrasound |
| title_full | Cortical plate segmentation using CNNs in 3D fetal ultrasound |
| title_fullStr | Cortical plate segmentation using CNNs in 3D fetal ultrasound |
| title_full_unstemmed | Cortical plate segmentation using CNNs in 3D fetal ultrasound |
| title_short | Cortical plate segmentation using CNNs in 3D fetal ultrasound |
| title_sort | cortical plate segmentation using cnns in 3d fetal ultrasound |
| work_keys_str_mv | AT wyburdmk corticalplatesegmentationusingcnnsin3dfetalultrasound AT jenkinsonm corticalplatesegmentationusingcnnsin3dfetalultrasound AT nambureteail corticalplatesegmentationusingcnnsin3dfetalultrasound |