Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey

Author:

Huang Sheng-YaoORCID,Hsu Wen-Lin,Hsu Ren-JunORCID,Liu Dai-Wei

Abstract

There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. This study retrospectively reviews recent studies on the application of deep learning for segmentation tasks in medical imaging and proposes potential directions for future development, including model development, data augmentation processing, and dataset creation. The strengths and deficiencies of studies on models and data augmentation, as well as their application to medical image segmentation, were analyzed. Fully convolutional network developments have led to the creation of the U-Net and its derivatives. Another noteworthy image segmentation model is DeepLab. Regarding data augmentation, due to the low data volume of medical images, most studies focus on means to increase the wealth of medical image data. Generative adversarial networks (GAN) increase data volume via deep learning. Despite the increasing types of medical image datasets, there is still a deficiency of datasets on specific problems, which should be improved moving forward. Considering the wealth of ongoing research on the application of deep learning processing to medical image segmentation, the data volume and practical clinical application problems must be addressed to ensure that the results are properly applied.

Funder

Buddhist Tzu Chi Medical Foundation

Publisher

MDPI AG

Subject

Clinical Biochemistry

Reference95 articles.

1. Statistical Models of Appearance for Medical Image Analysis and Computer Vision;Med. Imaging,2001

2. Gradient-Based Learning Applied to Document Recognition;Proc. IEEE,1998

3. ImageNet Classification with Deep Convolutional Neural Networks;Commun. ACM,2017

4. A novel approach for tuberculosis screening based on deep convolutional neural networks;Med. Imaging,2016

5. Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection;IEEE Trans. Biomed. Eng.,2017

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