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

by   Danielle F. Pace, et al.

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.


page 4

page 8


Accurate Congenital Heart Disease ModelGeneration for 3D Printing

3D printing has been widely adopted for clinical decision making and int...

Accurate Congenital Heart Disease Model Generation for 3D Printing

3D printing has been widely adopted for clinical decision making and int...

Joint Deep Learning for Improved Myocardial Scar Detection from Cardiac MRI

Automated identification of myocardial scar from late gadolinium enhance...

Cascaded Framework for Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI

Automatic evaluation of myocardium and pathology plays an important role...

Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data

Cardiovascular disease, the leading cause of death globally, is an age-r...

Fully Automatic Segmentation of Sublingual Veins from Retrained U-Net Model for Few Near Infrared Images

Sublingual vein is commonly used to diagnose the health status. The widt...

Duke Spleen Data Set: A Publicly Available Spleen MRI and CT dataset for Training Segmentation

Spleen volumetry is primarily associated with patients suffering from ch...

Please sign up or login with your details

Forgot password? Click here to reset