LF-SegNet: A Fully Convolutional Encoder–Decoder Network for Segmenting Lung Fields from Chest Radiographs

Author:

Mittal Ajay,Hooda Rahul,Sofat Sanjeev

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications

Reference49 articles.

1. Alexander Kalinovsky, A., & Kovalev, V. (2016). Lung image segmentation using deep learning methods and convolutional neural networks. In XIII International Conference on Pattern Recognition and Information Processing, Minsk: Publishing Center of BSU.

2. Annangi, P., Thiruvenkadam, S., Raja, A., Xu, H., Sun, X., & Mao, L. (2010). A region based active contour method for x-ray lung segmentation using prior shape and low level features. In 2010 IEEE international symposium on biomedical imaging: from nano to macro, pp. 892–895.

3. Arbabshirani, M. R., Dallal, A. H., Agarwal, C., Patel, A., & Moore, G. (2017). Accurate segmentation of lung fields on chest radiographs using deep convolutional networks. In SPIE medical imaging (pp. 1013,305–1013,305). International Society for Optics and Photonics.

4. Armato, S. G., Giger, M. L., & MacMahon, H. (1998). Automated lung segmentation in digitized posteroanterior chest radiographs. Academic Radiology, 5(4), 245–255.

5. Badrinarayanan, V., Handa, A., & Cipolla, R. (2015). Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. arXiv preprint arXiv:1505.07293

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