Abstract
We present a new method for end-to-end automatic volumetric segmentation of fetal structures in MRI scans with deep learning networks trained with very few annotated scans. It consists of three main stages: 1) two-step automatic structure segmentation with custom 3D U-Nets; 2) segmentation error estimation, and; 3) segmentation error correction. The automatic structure segmentation stage first computes a region of interest (ROI) on a downscaled scan and then computes a final segmentation on the cropped ROI. The segmentation error estimation stage uses prediction-time augmentations of the input scan to compute multiple segmentations and estimate the segmentation uncertainty for individual slices and for the entire scan. The segmentation error correction stage then uses these estimations to locate the most error-prone slices and to correct the segmentations in those slices based on validated adjacent slices. Experimental results of our methods on fetal body (63 cases, 9 for training, 55 for testing) and fetal brain MRI scans (35 cases, 6 for training, 29 for testing) yield a mean Dice coefficient of 0.96 for both, and a mean Average Symmetric Surface Distance of 0.74 mm and 0.19 mm, respectively, below the observer delineation variability.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings |
Editors | Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 365-374 |
Number of pages | 10 |
ISBN (Print) | 9783030597245 |
DOIs | |
State | Published - 2020 |
Event | 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: 4 Oct 2020 → 8 Oct 2020 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12266 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 |
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Country/Territory | Peru |
City | Lima |
Period | 4/10/20 → 8/10/20 |
Bibliographical note
Publisher Copyright:© 2020, Springer Nature Switzerland AG.
Keywords
- Deep learning
- Fetal MRI
- Segmentation
- Uncertainty estimation