A Stacked Generalization U-shape network based on zoom strategy and its application in biomedical image segmentation

Abstract

Background and objective: The deep neural network model can learn complex non-linear relationships in the data and has superior flexibility and adaptability. A downside of this flexibility is that they are sensitive to initial conditions, both in terms of the initial random weights and in terms of the statistical noise in the training dataset. And the disadvantage caused by adaptability is that deep convolutional networks usually have poor robustness or generalization when the models are trained using the extremely limited amount of labeled data, especially in the biomedical imaging informatics field.Methods: In this paper, we propose to develop and test a stacked generalization U-shape network (SG-UNet) based on the zoom strategy applying to biomedical image segmentation. SG-UNet is essentially a stacked generalization architecture consisting of multiple sub-modules, which takes multi-resolution images as input and uses hybrid features to segment regions of interest and detect diseases under the multi-supervision. The proposed new SG-UNet applies the zoom of multi-supervision to do optimization search in global feature space without pre-training. Besides, the zoom loss function can gradually enhance the focus training on a sparse set of hard samples.Results: We evaluated the proposed algorithm in comparison with several popular U-shape ensemble network architectures across multi-modal biomedical image segmentation tasks to segment malignant rectal cancers, polyps and glands from the three imaging modalities of computed tomography (CT), digital colonoscopy and histopathology images. Applying the proposed algorithm improves 3.116%, 2.676%, 2.356% on Dice coefficients, and 3.044%, 2.420%, 1.928% on F2-score for the three imaging modality datasets, respectively. The comparison results using different amounts of rectal cancer CT data show that the proposed algorithm has a slower tendency of diminishing marginal efficiency. And glands segmentation study results also support the feasibility of yielding comparable performance with other state-of-the-art methods.Conclusions: The proposed algorithm can be trained more efficiently by using the small image datasets without using additional techniques such as fine-tuning, and achieves higher accuracy with less computational complexity than other stacked ensemble networks for biomedical image segmentation.