Brain mr image enhancement for tumor segmentation using 3d u-net
Author | Ullah, Faizad |
Author | Ansari, Shahab U. |
Author | Hanif, Muhammad |
Author | Ayari, Mohamed A. |
Author | Chowdhury, Muhammad E. |
Author | Khandakar, Amith A. |
Author | Khan, Muhammad S. |
Available date | 2023-04-17T06:57:45Z |
Publication Date | 2021 |
Publication Name | Sensors |
Resource | Scopus |
Abstract | MRI images are visually inspected by domain experts for the analysis and quantification of the tumorous tissues. Due to the large volumetric data, manual reporting on the images is subjective, cumbersome, and error prone. To address these problems, automatic image analysis tools are employed for tumor segmentation and other subsequent statistical analysis. However, prior to the tumor analysis and quantification, an important challenge lies in the pre-processing. In the present study, permutations of different pre-processing methods are comprehensively investigated. In particular, the study focused on Gibbs ringing artifact removal, bias field correction, intensity normalization, and adaptive histogram equalization (AHE). The pre-processed MRI data is then passed onto 3D U-Net for automatic segmentation of brain tumors. The segmentation results demonstrated the best performance with the combination of two techniques, i.e., Gibbs ringing artifact removal and bias-field correction. The proposed technique achieved mean dice score metrics of 0.91, 0.86, and 0.70 for the whole tumor, tumor core, and enhancing tumor, respectively. The testing mean dice scores achieved by the system are 0.90, 0.83, and 0.71 for the whole tumor, core tumor, and enhancing tumor, respectively. The novelty of this work concerns a robust pre-processing sequence for improving the segmentation accuracy of MR images. The proposed method overcame the testing dice scores of the state-of-the-art methods. The results are benchmarked with the existing techniques used in the Brain Tumor Segmentation Challenge (BraTS) 2018 challenge. 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
Sponsor | Funding: The authors also thank the Higher Education Commission (HEC), Pakistan, for funding this research under the Artificial Intelligence in Healthcare, IIPL, National Center of Artificial Intelligence (NCAI) and partial funding by HEC-SRGP research grant. |
Language | en |
Publisher | MDPI |
Subject | Brain tumor segmentation Deep learning Gibbs ringing artifact Image enhancement Medical image processing |
Type | Article |
Issue Number | 22 |
Volume Number | 21 |
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Civil & Architectural Engineering [767 items ]
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Electrical Engineering [2568 items ]
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