research-article

Adaptive Weighted Loss Makes Brain Tumors Segmentation More Accurate in 3D MRI Volume

Authors:

Jianhua CaiAuthors Info & Claims

BDE '20: Proceedings of the 2020 2nd International Conference on Big Data Engineering

Pages 29 - 35

https://doi.org/10.1145/3404512.3404518

Published: 05 July 2020 Publication History

Abstract

Accurately segmenting brain tumors in Magnetic Resonance Imaging (MRI) volume can benefit the diagnosis, monitoring, and surgery planning of the disease. Manual delineation practices require anatomical knowledge, are expensive, time consuming and can be inaccurate due to human error. In the data era, machines with learning capabilities can achieve automatic brain tumor segmentation in MRI volume with promising performance on large region. However, it is not very effective for tumor segmentation on small region. Although more and more methods use pixel-level loss to guide algorithms to pay more attention to accurate segmentation of small regions, this problem still exists. In this paper, we propose an adaptive weighted loss, which can automatically adjust the proportion of loss generated by different region segmentation, thereby making small region segmentation more accurate. We added the adaptive weighted loss to a 3D MRI brain tumor segmentation network using auto-encoder regularization (3D-AE), and performed extensive validation on the MICCAI Brain Tumor Segmentation Challenge 2018 dataset (BRATS 2018). The achieved dice score is 0.769 for core tumor, 0.904 for the whole tumor and 0.887 for enhanced tumor. The overall results show better performance than the state-of-the-art in both dice score and precision on BRATS 2018.

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Cited By

Guru Karthik Reddy MJagadeesh PKiran Kumar P(2022)Innovative Detection of Brain Tumor using Conventional Neural Networks Classifier and comparison with Support Vector Machine Classifier2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)10.1109/MACS56771.2022.10022328(1-5)Online publication date: 12-Nov-2022
https://doi.org/10.1109/MACS56771.2022.10022328

Index Terms

Adaptive Weighted Loss Makes Brain Tumors Segmentation More Accurate in 3D MRI Volume
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation

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BDE '20: Proceedings of the 2020 2nd International Conference on Big Data Engineering

May 2020

146 pages

ISBN:9781450377225

DOI:10.1145/3404512

Copyright © 2020 ACM.

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Published: 05 July 2020

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BDE 2020: 2020 2nd International Conference on Big Data Engineering

May 29 - 31, 2020

Shanghai, China

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Cited By

Guru Karthik Reddy MJagadeesh PKiran Kumar P(2022)Innovative Detection of Brain Tumor using Conventional Neural Networks Classifier and comparison with Support Vector Machine Classifier2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)10.1109/MACS56771.2022.10022328(1-5)Online publication date: 12-Nov-2022
https://doi.org/10.1109/MACS56771.2022.10022328

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