Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net

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 tum...

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Autores principales:	Faizad Ullah, Shahab U. Ansari, Muhammad Hanif, Mohamed Arselene Ayari, Muhammad Enamul Hoque Chowdhury, Amith Abdullah Khandakar, Muhammad Salman Khan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	brain tumor segmentation deep learning Gibbs ringing artifact image enhancement medical image processing Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/a3a21b2648b641f7bead0236f81207a2
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spelling	oai:doaj.org-article:a3a21b2648b641f7bead0236f81207a22021-11-25T18:57:10ZBrain MR Image Enhancement for Tumor Segmentation Using 3D U-Net10.3390/s212275281424-8220https://doaj.org/article/a3a21b2648b641f7bead0236f81207a22021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7528https://doaj.org/toc/1424-8220MRI 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.Faizad UllahShahab U. AnsariMuhammad HanifMohamed Arselene AyariMuhammad Enamul Hoque ChowdhuryAmith Abdullah KhandakarMuhammad Salman KhanMDPI AGarticlebrain tumor segmentationdeep learningGibbs ringing artifactimage enhancementmedical image processingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7528, p 7528 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	brain tumor segmentation deep learning Gibbs ringing artifact image enhancement medical image processing Chemical technology TP1-1185
spellingShingle	brain tumor segmentation deep learning Gibbs ringing artifact image enhancement medical image processing Chemical technology TP1-1185 Faizad Ullah Shahab U. Ansari Muhammad Hanif Mohamed Arselene Ayari Muhammad Enamul Hoque Chowdhury Amith Abdullah Khandakar Muhammad Salman Khan Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net
description	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.
format	article
author	Faizad Ullah Shahab U. Ansari Muhammad Hanif Mohamed Arselene Ayari Muhammad Enamul Hoque Chowdhury Amith Abdullah Khandakar Muhammad Salman Khan
author_facet	Faizad Ullah Shahab U. Ansari Muhammad Hanif Mohamed Arselene Ayari Muhammad Enamul Hoque Chowdhury Amith Abdullah Khandakar Muhammad Salman Khan
author_sort	Faizad Ullah
title	Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net
title_short	Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net
title_full	Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net
title_fullStr	Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net
title_full_unstemmed	Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net
title_sort	brain mr image enhancement for tumor segmentation using 3d u-net
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a3a21b2648b641f7bead0236f81207a2
work_keys_str_mv	AT faizadullah brainmrimageenhancementfortumorsegmentationusing3dunet AT shahabuansari brainmrimageenhancementfortumorsegmentationusing3dunet AT muhammadhanif brainmrimageenhancementfortumorsegmentationusing3dunet AT mohamedarseleneayari brainmrimageenhancementfortumorsegmentationusing3dunet AT muhammadenamulhoquechowdhury brainmrimageenhancementfortumorsegmentationusing3dunet AT amithabdullahkhandakar brainmrimageenhancementfortumorsegmentationusing3dunet AT muhammadsalmankhan brainmrimageenhancementfortumorsegmentationusing3dunet
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Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net

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