Comparative validation of graphical models for learning tumor segmentations from noisy manual annotations

Abstract : Classification-based approaches for segmenting medical images commonly suffer from missing ground truth: often one has to resort to manual labelings by human experts, which may show considerable intra-rater and inter-rater variability. We experimentally evaluate several latent class and latent score models for tumor classification based on manual segmentations of different quality, using approximate variational techniques for inference. For the first time, we also study models that make use of image feature information on this specific task. Additionally, we analyze the outcome of hybrid techniques formed by combining aspects of different models. Benchmarking results on simulated MR images of brain tumors are presented: while simple baseline techniques already gave very competitive performance, significant improvements could be made by explicitly accounting for rater quality. Furthermore, we point out the transfer of these models to the task of fusing manual tumor segmentations derived from different imaging modalities on real-world data.
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Communication dans un congrès
Bjoern Menze and Georg Langs and Zhuowen Tu and Antonio Criminisi. MICCAI Workshop on Medical Computer Vision (MICCAI-MCV'10), 2010, Beijing, China. Springer, 6533, pp.74-85, 2010, Lecture Notes in Computer Science. 〈10.1007/978-3-642-18421-5_8〉
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https://hal.inria.fr/hal-00813770
Contributeur : Project-Team Asclepios <>
Soumis le : mardi 16 avril 2013 - 11:06:42
Dernière modification le : jeudi 11 janvier 2018 - 16:41:47

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Frederik O. Kaster, Bjoern H. Menze, Marc-André Weber, Fred A. Hamprecht. Comparative validation of graphical models for learning tumor segmentations from noisy manual annotations. Bjoern Menze and Georg Langs and Zhuowen Tu and Antonio Criminisi. MICCAI Workshop on Medical Computer Vision (MICCAI-MCV'10), 2010, Beijing, China. Springer, 6533, pp.74-85, 2010, Lecture Notes in Computer Science. 〈10.1007/978-3-642-18421-5_8〉. 〈hal-00813770〉

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