Automatic differential segmentation of the prostate in 3-D MRI using Random Forest classification and graph-cuts optimization

Emmanouil Moschidis, Jim Graham

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    In this paper we address the problem of automated differential segmentation of the prostate in three dimensional (3-D) magnetic resonance images (MRI) of patients with benign prostatic hyperplasia (BPH). We suggest a framework that consists of two stages: in the first stage, a Random Forest classifier localizes the anatomy of interest. In the second stage, Graph-Cuts (GC) optimization is utilized for obtaining the final delineation. GC optimization regularizes the hypotheses produced by the classification scheme by imposing contextual constraints via a Markov Random Field model. Our method obtains comparable or better results in a fully automated fashion compared with a previous semi-automatic technique [6]. It also performs well, when small training sets are used. This is particularly useful in on-line interactive segmentation systems, where prior knowledge is limited, or in automated approaches that generate ground truth used for model-building. © 2012 IEEE.
    Original languageEnglish
    Title of host publicationProceedings - International Symposium on Biomedical Imaging|IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn.
    PublisherIEEE
    Pages1727-1730
    Number of pages3
    ISBN (Print)9781457718588
    DOIs
    Publication statusPublished - 2012
    Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona
    Duration: 1 Jul 2012 → …

    Conference

    Conference2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
    CityBarcelona
    Period1/07/12 → …

    Keywords

    • Automatic Segmentation
    • Graph-Cuts
    • MRI
    • Prostate Zones
    • Random Forests

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