Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease
Name
iterativeSegInCHD.pdf
Description
Accepted version
Size
1.54 MB
Format
Unknown
Checksum (MD5)
7b179d0a1e44268f90b5873a8fd1e086
Author(s)
Pace, Danielle Frances
Dalca, Adrian Vasile
Brosch, Tom
Geva, Tal
Powell, Andrew J.
Weese, Jürgen
Moghari, Mehdi H.
Golland, Polina
Date Issued
2018
Publisher
Springer International Publishing
Citation
Pace, Danielle F., Dalca, Adrian V., Brosch, Tom, Geva, Tal, Powell, Andrew J. et al. 2018. "Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease."
Version
Author's final manuscript
Abstract
© Springer Nature Switzerland AG 2018. We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the intermediate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incomplete and/or inaccurate input segmentations paired with a recommended next step. Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes. We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels. Compared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-030-00889-5_38
