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3D brain tumor segmentation using a two-stage optimal mass transport algorithm
Optimal mass transport (OMT) theory, the goal of which is to move any irregular 3D object (i.e., the brain) without causing significant distortion, is used to preprocess brain tumor datasets for the first time in this paper. The first stage of a two-stage OMT (TSOMT) procedure transforms the brain i...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8355223/ https://www.ncbi.nlm.nih.gov/pubmed/34376714 http://dx.doi.org/10.1038/s41598-021-94071-1 |
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author | Lin, Wen-Wei Juang, Cheng Yueh, Mei-Heng Huang, Tsung-Ming Li, Tiexiang Wang, Sheng Yau, Shing-Tung |
author_facet | Lin, Wen-Wei Juang, Cheng Yueh, Mei-Heng Huang, Tsung-Ming Li, Tiexiang Wang, Sheng Yau, Shing-Tung |
author_sort | Lin, Wen-Wei |
collection | PubMed |
description | Optimal mass transport (OMT) theory, the goal of which is to move any irregular 3D object (i.e., the brain) without causing significant distortion, is used to preprocess brain tumor datasets for the first time in this paper. The first stage of a two-stage OMT (TSOMT) procedure transforms the brain into a unit solid ball. The second stage transforms the unit ball into a cube, as it is easier to apply a 3D convolutional neural network to rectangular coordinates. Small variations in the local mass-measure stretch ratio among all the brain tumor datasets confirm the robustness of the transform. Additionally, the distortion is kept at a minimum with a reasonable transport cost. The original [Formula: see text] dataset is thus reduced to a cube of [Formula: see text] , which is a 76.6% reduction in the total number of voxels, without losing much detail. Three typical U-Nets are trained separately to predict the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) from the cube. An impressive training accuracy of 0.9822 in the WT cube is achieved at 400 epochs. An inverse TSOMT method is applied to the predicted cube to obtain the brain results. The conversion loss from the TSOMT method to the inverse TSOMT method is found to be less than one percent. For training, good Dice scores (0.9781 for the WT, 0.9637 for the TC, and 0.9305 for the ET) can be obtained. Significant improvements in brain tumor detection and the segmentation accuracy are achieved. For testing, postprocessing (rotation) is added to the TSOMT, U-Net prediction, and inverse TSOMT methods for an accuracy improvement of one to two percent. It takes 200 seconds to complete the whole segmentation process on each new brain tumor dataset. |
format | Online Article Text |
id | pubmed-8355223 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83552232021-08-11 3D brain tumor segmentation using a two-stage optimal mass transport algorithm Lin, Wen-Wei Juang, Cheng Yueh, Mei-Heng Huang, Tsung-Ming Li, Tiexiang Wang, Sheng Yau, Shing-Tung Sci Rep Article Optimal mass transport (OMT) theory, the goal of which is to move any irregular 3D object (i.e., the brain) without causing significant distortion, is used to preprocess brain tumor datasets for the first time in this paper. The first stage of a two-stage OMT (TSOMT) procedure transforms the brain into a unit solid ball. The second stage transforms the unit ball into a cube, as it is easier to apply a 3D convolutional neural network to rectangular coordinates. Small variations in the local mass-measure stretch ratio among all the brain tumor datasets confirm the robustness of the transform. Additionally, the distortion is kept at a minimum with a reasonable transport cost. The original [Formula: see text] dataset is thus reduced to a cube of [Formula: see text] , which is a 76.6% reduction in the total number of voxels, without losing much detail. Three typical U-Nets are trained separately to predict the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) from the cube. An impressive training accuracy of 0.9822 in the WT cube is achieved at 400 epochs. An inverse TSOMT method is applied to the predicted cube to obtain the brain results. The conversion loss from the TSOMT method to the inverse TSOMT method is found to be less than one percent. For training, good Dice scores (0.9781 for the WT, 0.9637 for the TC, and 0.9305 for the ET) can be obtained. Significant improvements in brain tumor detection and the segmentation accuracy are achieved. For testing, postprocessing (rotation) is added to the TSOMT, U-Net prediction, and inverse TSOMT methods for an accuracy improvement of one to two percent. It takes 200 seconds to complete the whole segmentation process on each new brain tumor dataset. Nature Publishing Group UK 2021-08-10 /pmc/articles/PMC8355223/ /pubmed/34376714 http://dx.doi.org/10.1038/s41598-021-94071-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Lin, Wen-Wei Juang, Cheng Yueh, Mei-Heng Huang, Tsung-Ming Li, Tiexiang Wang, Sheng Yau, Shing-Tung 3D brain tumor segmentation using a two-stage optimal mass transport algorithm |
title | 3D brain tumor segmentation using a two-stage optimal mass transport algorithm |
title_full | 3D brain tumor segmentation using a two-stage optimal mass transport algorithm |
title_fullStr | 3D brain tumor segmentation using a two-stage optimal mass transport algorithm |
title_full_unstemmed | 3D brain tumor segmentation using a two-stage optimal mass transport algorithm |
title_short | 3D brain tumor segmentation using a two-stage optimal mass transport algorithm |
title_sort | 3d brain tumor segmentation using a two-stage optimal mass transport algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8355223/ https://www.ncbi.nlm.nih.gov/pubmed/34376714 http://dx.doi.org/10.1038/s41598-021-94071-1 |
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