Automatic brain image analysis based on multimodal deep learning scheme

Girija Chetty, Monica Singh, Matthew White

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

In this paper, we propose a new approach for brain image segmentation based on 3D U-Net deep learning architecture. The proposed approach takes into consideration both the neural network's optimizer as well the biological context of the segmentation tissue, by modeling the structured nature of glioma and edematous tissue around the enhancing and non-enhancing tumor within the U-net model. By training multiple deep neural networks based on 3D U-Nets, with a two-stage design, with whole tumor segmentation as the first stage, followed by segmentation of enhancing and non-enhancing tumors in the second stage, along with data augmentation, it was possible to build sparse deep learning model with few images, and achieve better tumor detection performance as compared to other deep learning models reported for BraTS 2018 challenge task, involving the usage of large dataset for building the models.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Machine Learning and Data Engineering (iCMLDE 2019)
EditorsPhill Kyu Rhee, Kuo-Yuan Hwa, Tun-Wen Pai, Daniel Howard, Rezaul Bashar
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages97-100
Number of pages4
ISBN (Electronic)9781728161198
ISBN (Print)9781728104041
DOIs
Publication statusPublished - 2 Dec 2019
EventInternational Conference on Machine Learning and Data Engineering 2019 - Taipei, Taiwan, Province of China
Duration: 2 Dec 20194 Dec 2019

Publication series

NameProceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019

Conference

ConferenceInternational Conference on Machine Learning and Data Engineering 2019
Abbreviated titleiCMLDE 2019
CountryTaiwan, Province of China
CityTaipei
Period2/12/194/12/19

Fingerprint Dive into the research topics of 'Automatic brain image analysis based on multimodal deep learning scheme'. Together they form a unique fingerprint.

  • Cite this

    Chetty, G., Singh, M., & White, M. (2019). Automatic brain image analysis based on multimodal deep learning scheme. In P. K. Rhee, K-Y. Hwa, T-W. Pai, D. Howard, & R. Bashar (Eds.), Proceedings - 2019 International Conference on Machine Learning and Data Engineering (iCMLDE 2019) (pp. 97-100). [8995755] (Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2019). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/iCMLDE49015.2019.00028