DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation.

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ID: 82933
2019
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Abstract
Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this article. First, we extract the region of interest from the raw medical images. Then, data augmentation is operated to acquire more training datasets. Our proposed method contains three models: encoder, U-net, and decoder. Encoder is mainly responsible for feature extraction of 2D image slice. The U-net cascades the features of each block of the encoder with those obtained by deconvolution in the decoder under different scales. The decoding is mainly responsible for the upsampling of the feature graph after feature extraction of each group. Simulation results show that the new method can boost the segmentation accuracy. And, it has strong robustness compared with other segmentation methods.
Reference Key
teng2019dmcnnjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Teng, Lin;Li, Hang;Karim, Shahid;
Journal journal of healthcare engineering
Year 2019
DOI
10.1155/2019/8597606
URL
Keywords
radial artery cardiovascular monitoring corrected flow time fluid responsiveness hemodynamic monitoring

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