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Parameter Estimation for Personalization of Liver Tumor Radiofrequency Ablation Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Combining Multiple Expert Annotations Using Semi-supervised Learning and Graph Cuts for Crohn’s Disease Segmentation

Authors : Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Carl A. J. Puylaert, Jesica C. Makanyanga, Alex Menys, Rado Andriantsimiavona, Jaap Stoker, Stuart A. Taylor, Franciscus M. Vos, Joachim M. Buhmann

Published in: Abdominal Imaging. Computational and Clinical Applications

Publisher: Springer International Publishing

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Abstract

We propose a graph cut (GC) based approach for combining annotations from multiple experts and segmenting Crohns disease (CD) tissues in magnetic resonance (MR) images. Random forest (RF) based semi supervised learning (SSL) predicts missing expert labels while a novel self consistency (SC) score quantifies the reliability of each expert label and also serves as the penalty cost in a second order Markov random field (MRF) cost function. The final consensus label is obtained by GC optimization. Experimental results on synthetic images and real CD patient data show our final segmentation to be more accurate than those obtained by competing methods. It also highlights the effectiveness of SC score in quantifying expert reliability and accuracy of SSL in predicting missing labels.

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previous chapter Semi-automatic Crohn’s Disease Severity Estimation on MR Imaging
next chapter Automatic Assessment of Image Informativeness in Colonoscopy
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Metadata
Title
Combining Multiple Expert Annotations Using Semi-supervised Learning and Graph Cuts for Crohn’s Disease Segmentation
Authors
Dwarikanath Mahapatra
Peter J. Schüffler
Jeroen A. W. Tielbeek
Carl A. J. Puylaert
Jesica C. Makanyanga
Alex Menys
Rado Andriantsimiavona
Jaap Stoker
Stuart A. Taylor
Franciscus M. Vos
Joachim M. Buhmann
Copyright Year
2014
Book
Abdominal Imaging. Computational and Clinical Applications

Print ISBN: 978-3-319-13691-2

Electronic ISBN: 978-3-319-13692-9

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-13692-9_13

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