@inproceedings{
 title = {MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks},
 type = {inproceedings},
 year = {2018},
 keywords = {Brain tumor segmentation,Deep learning,Fully convolutional networks,MRI,Random forest,Texton},
 pages = {204-215},
 volume = {10670 LNCS},
 publisher = {Springer, Cham},
 id = {5543dc80-9515-3709-be80-8f3c460dc9b0},
 created = {2024-01-13T08:14:13.837Z},
 file_attached = {true},
 profile_id = {d5b9f595-4104-33d2-b640-e84dee1b7ded},
 last_modified = {2024-01-13T08:16:55.699Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {In this paper, we propose a learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine-learned and hand-crafted features. Fully convolutional networks (FCN) forms the machine-learned features and texton based histograms are considered as hand-crafted features. Random forest (RF) is used to classify the MRI image voxels into normal brain tissues and different parts of tumors. The volumetric features from the segmented tumor tissues and patient age applying to an RF is used to predict the survival time. The method was evaluated on MICCAI-BRATS 2017 challenge dataset. The mean Dice overlap measures for segmentation of validation dataset are 0.86, 0.78 and 0.66 for whole tumor, core and enhancing tumor, respectively. The validation Hausdorff values are 7.61, 8.70 and 3.76. For the survival prediction task, the classification accuracy, pairwise mean square error and Spearman rank are 0.485, 198749 and 0.334, respectively.},
 bibtype = {inproceedings},
 author = {Soltaninejad, Mohammadreza and Zhang, Lei and Lambrou, Tryphon and Yang, Guang and Allinson, Nigel and Ye, Xujiong},
 doi = {10.1007/978-3-319-75238-9_18},
 booktitle = {Medical Image Computing and Computer Assisted Intervention MICCAI 2017 Brainlesion Workshop}
}

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Fully convolutional networks (FCN) forms the machine-learned features and texton based histograms are considered as hand-crafted features. Random forest (RF) is used to classify the MRI image voxels into normal brain tissues and different parts of tumors. The volumetric features from the segmented tumor tissues and patient age applying to an RF is used to predict the survival time. The method was evaluated on MICCAI-BRATS 2017 challenge dataset. The mean Dice overlap measures for segmentation of validation dataset are 0.86, 0.78 and 0.66 for whole tumor, core and enhancing tumor, respectively. The validation Hausdorff values are 7.61, 8.70 and 3.76. For the survival prediction task, the classification accuracy, pairwise mean square error and Spearman rank are 0.485, 198749 and 0.334, respectively.","bibtype":"inproceedings","author":"Soltaninejad, Mohammadreza and Zhang, Lei and Lambrou, Tryphon and Yang, Guang and Allinson, Nigel and Ye, Xujiong","doi":"10.1007/978-3-319-75238-9_18","booktitle":"Medical Image Computing and Computer Assisted Intervention MICCAI 2017 Brainlesion Workshop","bibtex":"@inproceedings{\n title = {MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks},\n type = {inproceedings},\n year = {2018},\n keywords = {Brain tumor segmentation,Deep learning,Fully convolutional networks,MRI,Random forest,Texton},\n pages = {204-215},\n volume = {10670 LNCS},\n publisher = {Springer, Cham},\n id = {5543dc80-9515-3709-be80-8f3c460dc9b0},\n created = {2024-01-13T08:14:13.837Z},\n file_attached = {true},\n profile_id = {d5b9f595-4104-33d2-b640-e84dee1b7ded},\n last_modified = {2024-01-13T08:16:55.699Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {In this paper, we propose a learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine-learned and hand-crafted features. 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