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Multi Planar Conditional Generative Adversarial Networks

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thesis
posted on 30.07.2021, 02:51 by Somosmita MitraSomosmita Mitra
Brain tumor sub region segmentation is a challenging problem in Magnetic Resonance imaging. The tumor regions tend to suffer from lack of homogeneity, textural differences, variable location, and their ability to proliferate into surrounding tissue.
The segmentation task thus requires an algorithm which can be indifferent to such influences and robust to external interference. In this work we propose a conditional generative adversarial network which learns off multiple planes of reference. Using this learning, we evaluate the quality of the segmentation and back propagate the loss for improving the learning. The results produced by the network show competitive quality in both the training and the testing data-set.

History

Degree Type

Master of Science

Department

Electrical and Computer Engineering

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Thomas Talavage

Additional Committee Member 2

Chris Brinton

Additional Committee Member 3

Mary Comer