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Published on: February 22, 2016
Semi-automatic segmentation of multiple mouse embryos in MR images
Leila Baghdadi1, Mojdeh Zamyadi, John G Sled
1Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Canada. baghdadi@phenogenomics.ca
BMC Bioinformatics
|June 18, 2011
Summary
This study introduces a novel algorithm for automatically segmenting multiple mouse embryos from MRI scans. The method enhances deformable models with balloon forces and collision detection for accurate 3D embryo phenotyping.
Area of Science:
- Medical Imaging
- Developmental Biology
- Computational Biology
Background:
- Automatic phenotyping of mouse embryos is crucial for developmental biology research.
- Magnetic Resonance Imaging (MRI) is used to scan multiple embryos within a single tube.
Purpose of the Study:
- To develop an algorithm for automatic phenotyping of mouse embryos using MRI data.
- To address limitations of existing deformable models in segmenting complex biological structures.
Main Methods:
- A modified simplex deformable model incorporating balloon forces for improved initialization.
- A novel automatic collision detection technique for simultaneous multi-object segmentation.
- Segmentation of 3D MRI data from multiple mouse embryos.
Main Results:
- The algorithm successfully segmented mouse embryos, with minor errors excluding paws and tails.
- Demonstrated improved initialization and adaptation to boundary concavities.
- Validated against manual segmentation, showing high accuracy for embryo bodies.
Conclusions:
- A novel multiple object segmentation technique with collision detection was developed.
- The method enables accurate segmentation of up to 32 mouse embryos in a single MRI scan.
- This facilitates advanced automatic phenotyping of developing embryos.
