Abstract

The development of an image-based brain image segmentation system using UNet has the advantages of a network that does not contain a fully contained layer. These steps have involved modifying the fully convolutional networks architecture proposed and extending it to work with very few images and more precise Segmentation. UNet produces only a few features. However, Corpus Callosum Segmentation requires high features and detects the edge of the rostrum, the genu, the body, and the splenium to achieve higher performance. This paper proposes UNet ++ with A New Hybrid Region-Based Segmentation (NHRBS) as a new region-based network strategy by combining region-based Segmentation with UNet++ that were improving object detection in 2D Corpus Callosum object segmentation. Our test results show that NHRBS accomplished a dice coefficient of 0.99.

Original languageEnglish
Title of host publicationProceeding - 5th International Conference on Informatics and Computational Sciences, ICICos 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages260-265
Number of pages6
ISBN (Electronic)9781665438070
DOIs
Publication statusPublished - 2021
Event5th International Conference on Informatics and Computational Sciences, ICICos 2021 - Semarang, Indonesia
Duration: 24 Nov 202125 Nov 2021

Publication series

NameProceedings - International Conference on Informatics and Computational Sciences
Volume2021-November
ISSN (Print)2767-7087

Conference

Conference5th International Conference on Informatics and Computational Sciences, ICICos 2021
Country/TerritoryIndonesia
CitySemarang
Period24/11/2125/11/21

Keywords

  • 2D Segmentation
  • Brain MRI
  • Corpus Callosum
  • Thresholding
  • UNet++

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