Share Email Print
cover

Proceedings Paper

Generative adversarial networks for brain lesion detection
Author(s): Varghese Alex; Mohammed Safwan K. P.; Sai Saketh Chennamsetty; Ganapathy Krishnamurthi
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as “Real” or “Fake” respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

Paper Details

Date Published: 24 February 2017
PDF: 9 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330G (24 February 2017); doi: 10.1117/12.2254487
Show Author Affiliations
Varghese Alex, Indian Institute of Technology Madras (India)
Mohammed Safwan K. P., Indian Institute of Technology Madras (India)
Sai Saketh Chennamsetty, Indian Institute of Technology Madras (India)
Ganapathy Krishnamurthi, Indian Institute of Technology Madras (India)


Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

© SPIE. Terms of Use
Back to Top