[ITK] [ITK-users] New Submission: Endocardial 3D Ultrasound Segmentation using Autocontext Random Forests

[MIDAS Journal]

Hello,

A new submission has been added to the MIDAS Journal.

Title: Endocardial 3D Ultrasound Segmentation using Autocontext Random Forests
Authors: Keraudren K., Oktay O., Shi W., Hajnal J.V., Rueckert D.
Abstract: In this paper, we present the use of a generic image segmentation method, namely a succession of Random Forest classifiers in an autocontext framework, for the MICCAI 2014 Challenge on Endocardial 3D Ultrasound Segmentation (CETUS). The proposed method segments each frame independently in 90 sec, without requiring temporal information such as end-diastolic or end-systolic time points nor any registration. For better segmentation accuracy, non-local means denoising can be applied to the images at the cost of an increased run-time. The mean Dice score on the testing dataset was 84.4% without denoising and 86.4% with denoising. The originality of our approach lies in the introduction of two classes, the myocardium and the mitral valve, in addition to the left ventricle and the background classes, in order to gain contextual information for the segmentation task.

Download and review this publication at: http://hdl.handle.net/10380/3484

Generated by the MIDAS Journal

You are receiving this email because you asked to be informed by the MIDAS Journal for new submissions. To change your email preference visit http://www.midasjournal.org/ .
_____________________________________
Powered by www.kitware.com

Visit other Kitware open-source projects at
http://www.kitware.com/opensource/opensource.html

Kitware offers ITK Training Courses, for more information visit:
http://www.kitware.com/products/protraining.php

Please keep messages on-topic and check the ITK FAQ at:
http://www.itk.org/Wiki/ITK_FAQ

Follow this link to subscribe/unsubscribe:
http://public.kitware.com/mailman/listinfo/insight-users