Automatic Segmentation of Organs-at-Risk in Thoracic Computed Tomography Images Using Ensembled U-Net InceptionV3 Model
Author:
Affiliation:
1. School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India.
Publisher
Mary Ann Liebert Inc
Subject
Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modeling and Simulation
Link
https://www.liebertpub.com/doi/pdf/10.1089/cmb.2022.0248
Reference56 articles.
1. Ashok M, Gupta A. Deep learning-based techniques for the automatic segmentation of organs in thoracic computed tomography images: A Comparative study. In: International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE; 2021a; pp. 198–202.
2. A Systematic Review of the Techniques for the Automatic Segmentation of Organs-at-Risk in Thoracic Computed Tomography Images
3. Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm
4. Cancer and Radiation Therapy: Current Advances and Future Directions
5. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
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