Brain Tumor Segmentation using U-Net and SegNet
- DOI
- 10.2991/978-94-6463-529-4_18How to use a DOI?
- Keywords
- U-Net; SegNet; Convolutional Neural Network (CNN); Brain tumor; segmentation; Magnetic Resonance Imaging (MRI)
- Abstract
The detection of tumors is the most difficult aspect of quantitative brain tumor evaluation. Magnetic Resonance Imaging (MRI) has gained popularity in recent years due to its non-invasive and powerful soft tissue contrast. MRI is a frequent imaging method used to detect brain cancers. The MRI produces a tremendous amount of data. Heterogeneity, isointensity, and hypointensity are characteristics of tumors that impede manual segmentation in a reasonable amount of time, hence limiting the use of valid quantitative measurements in clinical practice. In clinical practice, manual segmentation tasks are time-intensive and their performance is heavily dependent on the operator’s level of expertise. Also required are accurate and automated tumor segmentation approaches; however, the high spatial and structural variability of brain tumors makes automatic segmentation a challenging task. This paper proposes fully automatic segmentation of brain tumors using convolutional neural networks with encoder-decoders. This work focuses on well-known deep neural networks for semantic segmentation, namely U-Net, and SegNet, for segmenting tumors from Brain MRI data. The networks are trained and evaluated using a publicly available standard dataset, with Dice Similarity Coefficient (DSC) as a metric for the entire predicted image (tumor and background). The average DSC for U-Net on the test dataset is 0.76, while the average DSC for SegNet is 0.67. The examination of results demonstrates that U-Net performs better than SegNet.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Pankaj Kasar AU - Shivajirao Jadhav AU - Vineet Kansal PY - 2024 DA - 2024/10/04 TI - Brain Tumor Segmentation using U-Net and SegNet BT - Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023) PB - Atlantis Press SP - 194 EP - 206 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-529-4_18 DO - 10.2991/978-94-6463-529-4_18 ID - Kasar2024 ER -