Performance of Ensemble Methods with 2D Pre-trained Deep Learning Networks for 3D MRI Brain Segmentation
Keywords:
2D ensemble, pre-trained models, 3D small medical data, various composed train data, brain segmentation.Abstract
Ensemble method has been shown a great success for 2D image segmentation, while 3D brain segmentation has received less attention using 2D pre-trained model. In this work, we present various 2D ensemble methods to utilize the 2D pre-trained models for the brain MRI segmentation task using given small medical 3D data. We perform a series of experiments by comparing several 2D single pre-trained models to build and analyze the various 2D ensemble methods. We evaluate the ensemble methods against 3D single scratch model in terms of accuracy, time, and crop size. In addition, we investigate the relationship between different compositions of train data and performance for semantic segmentation using MRBrainS18 train dataset. Experimental results demonstrate a significant improvement of the proposed ensemble method in comparison with existing methods using 3D CNN models for brain MRI segmentation.
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