Cargando…
Hippocampal subfields segmentation in brain MR images using generative adversarial networks
BACKGROUND: Segmenting the hippocampal subfields accurately from brain magnetic resonance (MR) images is a challenging task in medical image analysis. Due to the small structural size and the morphological complexity of the hippocampal subfields, the traditional segmentation methods are hard to obta...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341719/ https://www.ncbi.nlm.nih.gov/pubmed/30665408 http://dx.doi.org/10.1186/s12938-019-0623-8 |
_version_ | 1783389000873017344 |
---|---|
author | Shi, Yonggang Cheng, Kun Liu, Zhiwen |
author_facet | Shi, Yonggang Cheng, Kun Liu, Zhiwen |
author_sort | Shi, Yonggang |
collection | PubMed |
description | BACKGROUND: Segmenting the hippocampal subfields accurately from brain magnetic resonance (MR) images is a challenging task in medical image analysis. Due to the small structural size and the morphological complexity of the hippocampal subfields, the traditional segmentation methods are hard to obtain the ideal segmentation result. METHODS: In this paper, we proposed a hippocampal subfields segmentation method using generative adversarial networks. The proposed method can achieve the pixel-level classification of brain MR images by building an UG-net model and an adversarial model and training the two models against each other alternately. UG-net extracts local information and retains the interrelationship features between pixels. Moreover, the adversarial training implements spatial consistency among the generated class labels and smoothens the edges of class labels on segmented region. RESULTS: The evaluation has performed on the dataset obtained from center for imaging of neurodegenerative diseases (CIND) for CA1, CA2, DG, CA3, Head, Tail, SUB, ERC and PHG in hippocampal subfields, resulting in the dice similarity coefficient (DSC) of 0.919, 0.648, 0.903, 0.673, 0.929, 0.913, 0.906, 0.884 and 0.889 respectively. For the large subfields, such as Head and CA1 of hippocampus, the DSC was increased by 3.9% and 9.03% than state-of-the-art approaches, while for the smaller subfields, such as ERC and PHG, the segmentation accuracy was significantly increased 20.93% and 16.30% respectively. CONCLUSION: The results show the improvement in performance of the proposed method, compared with other methods, which include approaches based on multi-atlas, hierarchical multi-atlas, dictionary learning and sparse representation and CNN. In implementation, the proposed method provides better results in hippocampal subfields segmentation. |
format | Online Article Text |
id | pubmed-6341719 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63417192019-01-24 Hippocampal subfields segmentation in brain MR images using generative adversarial networks Shi, Yonggang Cheng, Kun Liu, Zhiwen Biomed Eng Online Research BACKGROUND: Segmenting the hippocampal subfields accurately from brain magnetic resonance (MR) images is a challenging task in medical image analysis. Due to the small structural size and the morphological complexity of the hippocampal subfields, the traditional segmentation methods are hard to obtain the ideal segmentation result. METHODS: In this paper, we proposed a hippocampal subfields segmentation method using generative adversarial networks. The proposed method can achieve the pixel-level classification of brain MR images by building an UG-net model and an adversarial model and training the two models against each other alternately. UG-net extracts local information and retains the interrelationship features between pixels. Moreover, the adversarial training implements spatial consistency among the generated class labels and smoothens the edges of class labels on segmented region. RESULTS: The evaluation has performed on the dataset obtained from center for imaging of neurodegenerative diseases (CIND) for CA1, CA2, DG, CA3, Head, Tail, SUB, ERC and PHG in hippocampal subfields, resulting in the dice similarity coefficient (DSC) of 0.919, 0.648, 0.903, 0.673, 0.929, 0.913, 0.906, 0.884 and 0.889 respectively. For the large subfields, such as Head and CA1 of hippocampus, the DSC was increased by 3.9% and 9.03% than state-of-the-art approaches, while for the smaller subfields, such as ERC and PHG, the segmentation accuracy was significantly increased 20.93% and 16.30% respectively. CONCLUSION: The results show the improvement in performance of the proposed method, compared with other methods, which include approaches based on multi-atlas, hierarchical multi-atlas, dictionary learning and sparse representation and CNN. In implementation, the proposed method provides better results in hippocampal subfields segmentation. BioMed Central 2019-01-21 /pmc/articles/PMC6341719/ /pubmed/30665408 http://dx.doi.org/10.1186/s12938-019-0623-8 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Shi, Yonggang Cheng, Kun Liu, Zhiwen Hippocampal subfields segmentation in brain MR images using generative adversarial networks |
title | Hippocampal subfields segmentation in brain MR images using generative adversarial networks |
title_full | Hippocampal subfields segmentation in brain MR images using generative adversarial networks |
title_fullStr | Hippocampal subfields segmentation in brain MR images using generative adversarial networks |
title_full_unstemmed | Hippocampal subfields segmentation in brain MR images using generative adversarial networks |
title_short | Hippocampal subfields segmentation in brain MR images using generative adversarial networks |
title_sort | hippocampal subfields segmentation in brain mr images using generative adversarial networks |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341719/ https://www.ncbi.nlm.nih.gov/pubmed/30665408 http://dx.doi.org/10.1186/s12938-019-0623-8 |
work_keys_str_mv | AT shiyonggang hippocampalsubfieldssegmentationinbrainmrimagesusinggenerativeadversarialnetworks AT chengkun hippocampalsubfieldssegmentationinbrainmrimagesusinggenerativeadversarialnetworks AT liuzhiwen hippocampalsubfieldssegmentationinbrainmrimagesusinggenerativeadversarialnetworks |