Echocardiographic image multi-structure segmentation using Cardiac-SegNet.

ABSTRACT:

Purpose

Cardiac boundary segmentation of echocardiographic images is important for cardiac function assessment and disease diagnosis. However, it is challenging to segment cardiac ventricles due to the low contrast-to-noise ratio and speckle noise of the echocardiographic images. Manual segmentation is subject to interobserver variability and is too slow for real-time image-guided interventions. We aim to develop a deep learning-based method for automated multi-structure segmentation of echocardiographic images.

Methods

We developed an anchor-free mask convolutional neural network (CNN), termed Cardiac-SegNet, which consists of three subnetworks, that is, a backbone, a fully convolutional one-state object detector (FCOS) head, and a mask head. The backbone extracts multi-lev

SUBMITTER: Lei Y 

PROVIDER: S-EPMC11698071 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature