<div>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. </div><div> 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.</div><div><br></div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/15070530 |
| Date | 30 July 2021 |
| Creators | Somosmita Mitra (11197152) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Multi_Planar_Conditional_Generative_Adversarial_Networks/15070530 |
Page generated in 0.0018 seconds