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Superpixel Region Merging Based on Deep Network for Medical Image Segmentation by Hui Liu; Haiou Wang; Yan Wu; Lei Xing is a Computer Science article available to read on EtoBox.

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Automatic and accurate semantic segmentation of pathological structures in medical images is challenging because of noisy disturbance, deformable shapes of pathology, and low contrast between soft tissues. Classical superpixel-based classification algorithms suffer from edge leakage due to complexity and heterogeneity inherent in medical images. Therefore, we propose a deep U-Net with superpixel region merging processing incorporated for edge enhancement to facilitate and optimize segmentation. Our approach combines three innovations: (1) different from deep learning--based image segmentation, the segmentation evolved from superpixel region merging via U-Net training getting rich semantic information, in addition to gray similarity; (2) a bilateral filtering module was adopted at the beginning of the network to eliminate external noise and enhance soft tissue contrast at edges of pathogy; and (3) a normalization layer was inserted after the convolutional layer at each feature scale, to prevent overfitting and increase the sensitivity to model parameters. This model was validated on lung CT, brain MR, and coronary CT datasets, respectively. Different superpixel methods and cross val

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Author

Hui Liu; Haiou Wang; Yan Wu; Lei Xing

Publisher

Association for Computing Machinery (ACM)

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)