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Paper number: 136 A deep learning approach to segmentation of the - - PowerPoint PPT Presentation
Paper number: 136 A deep learning approach to segmentation of the - - PowerPoint PPT Presentation
Paper number: 136 A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling AE Fetit , A Alansary, L Cordero-Grande, J Cupitt, AB Davidson, AD Edwards, JV Hajnal, E Hughes, K Kamnitsas, V
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Segmenta5on – Ul5mate Goal
Develop a 3D structural segmenta;on pipeline for fetal brain MRI to support connectomics research.
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- Rapid changes in morphology over narrow ;me-scales.
- Changes in white/grey-maKer intensi;es also take place.
Segmenta5on - Challenges
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Successful in other medical imaging applica;ons
- However, main difficulty is in the need for large annotated
ground-truth.
- Whilst large public datasets exist, they tend to mainly
include adult brain scans e.g. UK Biobank.
Deep Learning?
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Apply Draw-EM with fetal atlas to generate preliminary 3D labels Manual QC Train a multiclass 3D CNN using scans that passed QC step Apply the multiclass 3D CNN Give ~300 2D slices to expert annotator Refine the cortex labels Fine-tune the 3D cortex segmentation CNN
Minimal Labeling Workflow
Train a 3D cortex segmentation CNN
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Ka Kamnitsas e et al. 2016 t al. 2016
- 3D
3D mo modeling deling u usin sing g DeepMedic DeepMedic
CNN Architecture
- Th
Three parallel pathways:
- no
normal rmal reso solu;o lu;on n
- do
downsample wnsampled b by 3 y 3
- do
downsample wnsampled b by 5 y 5
- 8 la
8 layer ers per pa s per path thway y
- Tr
Training batch size was set to 5
- Learning r
Learning rate f e follo llowed a pr ed a pre-defined schedule. e-defined schedule.
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Preliminary mul5class labels
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Gestational age: 27.5 weeks
Example cortex refinement
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Example cortex segmenta5on
Gestational age: 28 weeks
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