Author:
Song Chongchong,He Baochun,Chen Hongyu,Jia Shuangfu,Chen Xiaoxia,Jia Fucang
Publisher
Springer International Publishing
Reference14 articles.
1. Huang, C., et al.: Fully automatic liver segmentation using a probability atlas registration. In: O’Conner, L. (ed.) Proceedings of ICECC, pp. 126–129. IEEE Computer Society Press (2012)
2. Xu, L.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663–2674 (2018)
3. Seo, H., Huang, C., Bassenne, M., Xiao, R., Xing, L.: Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images. IEEE Trans. Med. Imaging 39(5), 1316–1325 (2020)
4. Heimman, T., van Ginneken, B., Styner, M.A., et al.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imaging 28(8), 1251–1265 (2009)
5. Bilic, P., Christ, P.R., Vorontsov, E., et al.: The liver tumor segmentation benchmark (LiTS). arXiv reprint. arXiv: 1901.040506 (2019)
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