A more generalizable DNN based Automatic Segmentation of Brain Tumors from Multimodal low-resolution 2D MRI

B. DIvya, Rajesh Parameshwaran Nair, K. Prakashini, R. Girish Menon, Paul Litvak, Pitchaiah Mandava, Deepu Vijayasenan, S. Sumam David

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the field of Neuro-oncology, there is a need for improved diagnosis and prognosis of brain tumors. Brain tumor segmentation is important for treatment planning and assessing the treatment outcomes. Manual segmentation of brain tumors is tedious, time-consuming, and subjective. In this work, an efficient encoder-decoder based architectures were implemented for automatic segmentation of brain tumors from low resolution 2D images. Ensemble of the multiple architectures (EMMA) improves the performance of the brain tumor segmentation. Furthermore, the computational requirements of the proposed models are lower than that of BraTS-challenge methods. The average Fl-scores on the BraTS-challenge validation dataset for Tumor Core, Whole Tumor, and Enhancing Tumor are 0.82, 0.87, and 0.78, respectively. The average Fl-scores on the KMC-Manipal dataset for TC, WT, and ET are 0.74, 0.82, and 0.68 respectively.

Original languageEnglish
Title of host publicationProceedings of the 2021 IEEE 18th India Council International Conference, INDICON 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665441759
DOIs
Publication statusPublished - 2021
Event18th IEEE India Council International Conference, INDICON 2021 - Guwahati, India
Duration: 19-12-202121-12-2021

Publication series

NameProceedings of the 2021 IEEE 18th India Council International Conference, INDICON 2021

Conference

Conference18th IEEE India Council International Conference, INDICON 2021
Country/TerritoryIndia
CityGuwahati
Period19-12-2121-12-21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

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