Comparison of Patch-Based Conditional Generative Adversarial Neural Net Models with Emphasis on Model Robustness for Use in Head and Neck Cases for MR-Only planning

Autor: Klages, Peter, Benslimane, Ilyes, Riyahi, Sadegh, Jiang, Jue, Hunt, Margie, Deasy, Joe, Veeraraghavan, Harini, Tyagi, Neelam

A total of twenty paired CT and MR images were used in this study to investigate two conditional generative adversarial networks, Pix2Pix, and Cycle GAN, for generating synthetic CT images for Headand Neck cancer cases. Ten of the patient cases were

Externí odkaz: http://arxiv.org/abs/1902.00536

Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening

Autor: Choi, Wookjin, Nadeem, Saad, Riyahi, Sadegh, Deasy, Joseph O., Tannenbaum, Allen, Lu, Wei

Spiculations are important predictors of lung cancer malignancy, which are spikes on the surface of the pulmonary nodules. In this study, we proposed an interpretable and parameter-free technique to quantify the spiculation using area distortion metr

Externí odkaz: http://arxiv.org/abs/1808.08307

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Technical Note: Identification of CT Texture Features Robust to Tumor Size Variations for Normal Lung Texture Analysis.

Autor: Choi W; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065., Riyahi S; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065., Kligerman SJ; Department of Radiology, University of California at San Diego, San Diego, CA 92103., Liu CJ; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065., Mechalakos JG; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065., Lu W; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065.

Publikováno v: International journal of medical physics, clinical engineering and radiation oncology [Int J Med Phys Clin Eng Radiat Oncol] 2018 Aug; Vol. 7 (3), pp. 330-338. Date of Electronic Publication: 2018 Aug 07.