The Effect of Spectral Resolution Upon the Accuracy of Brain Tumor Segmentation from Multi-Spectral MRI Data

Ensemble learning methods are frequently employed for brain tumor segmentation from multi-spectral MRI data. These techniques often require involving several hundreds of computed features for the characterization of the voxels, causing a rise in the necessary storage space by two order of magnitude. Processing such amounts of data also represents a serious computational burden. Under such circumstances it is useful to optimize the feature generation process. This paper proposes to establish the optimal spectral resolution of multispectral MRI data based feature values that allows for the best achievable brain tumor segmentation accuracy without causing unnecessary computational load and storage space waste. Experiments revealed that an 8-bit spectral resolution of the MRI-based feature data is sufficient to obtain the best possible accuracy of ensemble learning methods, while it allows for 50% reduction of the storage space required by the segmentation procedure, compared to the usually deployed featured encoding techniques

Brain Tumor Segmentation from MRI Data Using Ensemble Learning and Multi-Atlas

Atlases are frequently employed to assist medical image segmentation with prior information. This paper introduces a multi-atlas architecture that is trained to locally characterize the appearance (average intensity and standard deviation) of normal tissues in various observed and computed data channels of brain MRI records. The multiple atlas is then deployed to enhance the accuracy of an ensemble learning based brain tumor segmentation procedure that uses binary decision trees. The proposed method is validated using the low-grade tumor volumes of the BraTS 2016 train data set. The use of atlases improve the segmentation quality, causing a rise of up to 1.5% in average Dices scores

Brain Tumor Segmentation from Multi-Spectral MRI Data Using Cascaded Ensemble Learning

Ensemble learning methods are frequently employed in medical decision support. In image segmentation problems the ensemble based decisions require a postprocessing, because the ensemble cannot adequately handle the strong correlation of neighbor voxels. This paper proposes a brain tumor segmentation procedure based on an ensemble cascade. The first ensemble consisting of binary decision trees is trained to separate focal lesions from normal tissues based on four observed and 100 computed features. Starting from the intermediary labels provided by the first ensemble, six local features are computed for each voxel that serve as input for the second ensemble. The second ensemble is a classical random forest that enforces the correlation between neighbor pixels, regularizes the shape of the lesions. The segmentation accuracy is characterized by 85.5% overall Dice Score, 0.5% above previous solutions