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ECR 2019 / C-2866
Cloud-based semi-automated liver segmentation: analytical study to compare its speed and accuracy with a semi-automated workstation based software
Congress: ECR 2019
Poster No.: C-2866
Type: Scientific Exhibit
Keywords: Artificial Intelligence, CT, Segmentation
Authors: V. Venugopal1, A. Chunduru2, M. Barnwal3, D. S. Mahra4, A. Raj2, K. Vaidhya2, A. Rangasai Devalla2, V. Mahajan3, H. Mahajan 3; 1Aligarh/IN, 2Bangalore/IN, 3New Delhi/IN, 4Bengaluru, Karnataka/IN



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5. Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., Greenspan, H., Fully Convolutional Network for Liver Segmentation and Lesions Detection 2016. Dlmia. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. Vol. 10008 of Lect Notes Comput Sci. pp. 77–85


6. Vaidhya, Kiran, Subramaniam Thirunavukkarasu, Alex Varghese and Ganapathy Krishnamurthi. “Multi-modal Brain Tumor Segmentation Using Stacked Denoising Autoencoders.” Brainles@MICCAI (2015).


7. Vaidya, S., et al.: Longitudinal multiple Sclerosis lesion segmentation using 3D convolutional neural networks. In: Proceedings of the 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pp. 1–2 (2015)


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9. O. Ronneberger, P. Fischer, T. Brox, "U-net: Convolutional networks for biomedical image segmentation", Proc. Int. Conf. Medical Image Comput. Comput.-Assisted Intervention, pp. 234-241, 2015.

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