Registration based data augmentation for multiple sclerosis lesion segmentation

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

Deep learning has shown outstanding performance on various computer vision tasks such as image segmentation. To take advantage of deep learning in image segmentation, one would need a huge amount of annotated data since deep learning models are data-intensive. One of the main challenges of using deep learning methods in the medical domain is the shortage of available annotated data. To tackle this problem, in this paper, we propose a registration based framework for augmenting multiple sclerosis datasets. In this framework, by registering images of two different patients, we create a new image, which smoothly adds lesions from the first patient into a brain image, structured like the second patient. Due to their nature, multiple sclerosis lesions vary in shape, size, location and number of occurrence, thus registering images of two different subjects, will create a realistic image. The proposed method is capable of introducing diversity to data distribution, which other traditional augmentation methods do not offer. To check the effectiveness of our proposed method, we compare the performance of 3D-Unet on different augmented and non-augmented datasets. Experimental results indicate that the best performance is achieved when combining both the proposed method with traditional augmentation techniques.

Original languageEnglish
Title of host publication2019 Digital Image Computing: Techniques and Applications, DICTA 2019
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-5
Number of pages5
ISBN (Electronic)9781728138572
DOIs
Publication statusPublished - 2019
Event2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019 - Perth, Australia
Duration: 2 Dec 20194 Dec 2019

Conference

Conference2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019
CountryAustralia
CityPerth
Period2/12/194/12/19

Keywords

  • Image Segmentation
  • Registration
  • Deep Learning
  • Convolutional Neural Networks
  • Data Augmentation

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