Low-Grade Gliomas MR Image Segmentation Based on Conditional Generative Adversarial Networks

Lingmei Ai; Kangzhen Shi

doi:10.3788/LOP57.221004

[1] Ostrom Q T, Gittleman H, Fulop J et al. --2012[J]. Neuro-Oncology, 2015, 17(suppl 4): iv1-iv62.(2008).

[2] Louis D N, Perry A, Reifenberger G et al. The 2016 world health organization classification of tumors of the central nervous system: a summary[J]. Acta Neuropathologica, 131, 803-820(2016). http://en.cnki.com.cn/Article_en/CJFDTotal-CGZC201612002.htm

[3] Menze B H, Jakab A, Bauer S et al. The multimodal brain tumor image segmentation benchmark (BRATS)[J]. IEEE Transactions on Medical Imaging, 34, 1993-2024(2015). http://pubmedcentralcanada.ca/pmcc/articles/PMC4833122/

[4] Havaei M, Davy A, Warde-Farley D et al. Brain tumor segmentation with deep neural networks[J]. Medical Image Analysis, 35, 18-31(2017). http://www.ncbi.nlm.nih.gov/pubmed/27310171/

[5] Shen T, Gou C, Wang F Y et al. Learning from adversarial medical images for X-ray breast mass segmentation[J]. Computer Methods and Programs in Biomedicine, 180, 105012(2019). http://www.researchgate.net/publication/334976993_Learning_from_adversarial_medical_images_for_X-ray_breast_mass_segmentation

[6] Buda M, Saha A, Mazurowski M A. Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm[J]. Computers in Biology and Medicine, 109, 218-225(2019).

[7] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. ∥Navab N, Hornegger J, Wells W, et al. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science. Cham: Springer, 9351, 234-241(2015).

[8] Greenspan H, van Ginneken B, Summers R M. Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique[J]. IEEE Transactions on Medical Imaging, 35, 1153-1159(2016).

[9] Pezeshk A, Tutwiler R L. Automatic feature extraction and text recognition from scanned topographic maps[J]. IEEE Transactions on Geoscience and Remote Sensing, 49, 5047-5063(2011).

[10] Liu K, Wang D, Rong M X. X-ray image classification algorithm based on semi-supervised generative adversarial networks[J]. Acta Optica Sinica, 39, 0810003(2019).

[11] Yang X L, Lin S Z, Lu X F et al. Multimodal image fusion based on generative adversarial networks[J]. Laser & Optoelectronics Progress, 56, 161004(2019).

[12] Li L F, Hu M. Method for small-bridge-crack segmentation based on generative adversarial network[J]. Laser & Optoelectronics Progress, 56, 101004(2019).

[13] Goodfellow I, Pouget-Abadie J, Mirza M et al[2020-01-02]. Generative adversarial nets [2020-01-02].https:∥arxiv., org/pdf/1406, 2661.

[14] Xue Y, Xu T, Zhang H et al. SegAN: adversarial network with multi-scale L1loss for medical image segmentation[J]. Neuroinformatics, 16, 383-392(2018).

[15] Mirza M, Osindero S[2020-01-03]. Conditional generative adversarial nets [2020-01-03].https:∥arxiv., org/pdf/1411, 1784.