Bayesian Network and Structured Random Forest Cooperative Deep Learning For Automatic Multi-label Brain Tumor Segmentation

Samya Amiri, Mohamed Ali Mahjoub, Islem Rekik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)


Brain cancer phenotyping and treatment is highly informed by radiomic analyses of medical images. Specifically, the reliability of radiomics, which refers to extracting features from the tumor image intensity, shape and texture, depends on the accuracy of the tumor boundary segmentation. Hence, developing fully-automated brain tumor segmentation methods is highly desired for processing large imaging datasets. In this work, we propose a cooperative learning framework for multi-label brain tumor segmentation, which leverages on Structured Random Forest (SRF) and Bayesian Networks (BN). Basically, we embed both strong SRF and BN classifiers into a multi-layer deep architecture, where they cooperate to better learn tumor features for our multi-label classification task. The proposed SRF-BN cooperative learning integrates two complementary merits of both classifiers. While, SRF exploits structural and contextual image information to perform classification at the pixel-level, BN represents the statistical dependencies between image components at the superpixel-level. To further improve this SRF-BN cooperative learning, we ‘deepen’ this cooperation through proposing a multilayer framework, wherein each layer, BN inputs the original multi-modal MR images along with the probability maps generated by SRF. Through transfer learning from SRF to BN, the performance of BN improves. In turn, in the next layer, SRF will also benefit from the learning of BN through inputting the BN segmentation maps along with the original multimodal images. With the exception of the first layer, both classifiers use the output segmentation maps resulting from the previous layer, in the spirit of auto context models. We evaluated our framework on 50 subjects with multimodal MR images (FLAIR, T1, T1-c) to segment the whole tumor, its core and enhanced tumor. Our segmentation results outperformed those of several comparison methods, including the independent (non-cooperative) learning of SRF and BN.
Original languageEnglish
Title of host publication Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018)
Number of pages8
Publication statusPublished - 2018
EventICAART 2018: 10th International Conference on Agents and Artificial Intelligence - Madeira, Portugal
Duration: 16 Jan 201818 Jan 2018


ConferenceICAART 2018
Internet address


  • Autocontext Model
  • Bayesian Network
  • Brain Tumor Segmentation
  • Deep Cooperative Network
  • MRIs
  • Structured Random Forest

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence


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