Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

TL;DR

This paper introduces an iterative segmentation model that learns from limited data by recursively refining segmentations, specifically improving heart structure segmentation in congenital heart disease from small MRI datasets.

Contribution

The novel iterative segmentation approach uses a recurrent neural network to evolve segmentations step-by-step, reducing the need for large annotated datasets.

Findings

More accurate segmentation in severe CHD cases

Effective with only 20 training images

Outperforms direct segmentation methods

Abstract

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 interme- diate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incom- plete 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. Com- pared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.