DeepAI AI Chat
Log In Sign Up

Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation

09/30/2018
by   Riddhish Bhalodia, et al.
THE UNIVERSITY OF UTAH
0

Left atrium shape has been shown to be an independent predictor of recurrence after atrial fibrillation (AF) ablation. Shape-based representation is imperative to such an estimation process, where correspondence-based representation offers the most flexibility and ease-of-computation for population-level shape statistics. Nonetheless, population-level shape representations in the form of image segmentation and correspondence models derived from cardiac MRI require significant human resources with sufficient anatomy-specific expertise. In this paper, we propose a machine learning approach that uses deep networks to estimate AF recurrence by predicting shape descriptors directly from MRI images, with NO image pre-processing involved. We also propose a novel data augmentation scheme to effectively train a deep network in a limited training data setting. We compare this new method of estimating shape descriptors from images with the state-of-the-art correspondence-based shape modeling that requires image segmentation and correspondence optimization. Results show that the proposed method and the current state-of-the-art produce statistically similar outcomes on AF recurrence, eliminating the need for expensive pre-processing pipelines and associated human labor.

READ FULL TEXT

page 2

page 4

10/14/2021

DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models

Statistical shape modeling (SSM) characterizes anatomical variations in ...
11/13/2021

Leveraging Unsupervised Image Registration for Discovery of Landmark Shape Descriptor

In current biological and medical research, statistical shape modeling (...
09/06/2022

Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach

Clinical investigations of anatomy's structural changes over time could ...
07/13/2020

Uncertain-DeepSSM: From Images to Probabilistic Shape Models

Statistical shape modeling (SSM) has recently taken advantage of advance...
10/17/2018

A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures

We propose a novel machine learning strategy for studying neuroanatomica...
11/24/2018

Matching Disparate Image Pairs Using Shape-Aware ConvNets

An end-to-end trainable ConvNet architecture, that learns to harness the...
02/21/2021

Learning Deep Features for Shape Correspondence with Domain Invariance

Correspondence-based shape models are key to various medical imaging app...