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DNL-Net: deformed non-local neural network for blood vessel segmentation
BACKGROUND: The non-local module has been primarily used in literature to capturing long-range dependencies. However, it suffers from prohibitive computational complexity and lacks the interactions among positions across the channels. METHODS: We present a deformed non-local neural network (DNL-Net)...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9169317/ https://www.ncbi.nlm.nih.gov/pubmed/35668351 http://dx.doi.org/10.1186/s12880-022-00836-z |
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author | Ni, Jiajia Wu, Jianhuang Elazab, Ahmed Tong, Jing Chen, Zhengming |
author_facet | Ni, Jiajia Wu, Jianhuang Elazab, Ahmed Tong, Jing Chen, Zhengming |
author_sort | Ni, Jiajia |
collection | PubMed |
description | BACKGROUND: The non-local module has been primarily used in literature to capturing long-range dependencies. However, it suffers from prohibitive computational complexity and lacks the interactions among positions across the channels. METHODS: We present a deformed non-local neural network (DNL-Net) for medical image segmentation, which has two prominent components; deformed non-local module (DNL) and multi-scale feature fusion. The former optimizes the structure of the non-local block (NL), hence, reduces the problem of excessive computation and memory usage, significantly. The latter is derived from the attention mechanisms to fuse the features of different levels and improve the ability to exchange information across channels. In addition, we introduce a residual squeeze and excitation pyramid pooling (RSEP) module that is like spatial pyramid pooling to effectively resample the features at different scales and improve the network receptive field. RESULTS: The proposed method achieved 96.63% and 92.93% for Dice coefficient and mean intersection over union, respectively, on the intracranial blood vessel dataset. Also, DNL-Net attained 86.64%, 96.10%, and 98.37% for sensitivity, accuracy and area under receiver operation characteristic curve, respectively, on the DRIVE dataset. CONCLUSIONS: The overall performance of DNL-Net outperforms other current state-of-the-art vessel segmentation methods, which indicates that the proposed network is more suitable for blood vessel segmentation, and is of great clinical significance. |
format | Online Article Text |
id | pubmed-9169317 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91693172022-06-07 DNL-Net: deformed non-local neural network for blood vessel segmentation Ni, Jiajia Wu, Jianhuang Elazab, Ahmed Tong, Jing Chen, Zhengming BMC Med Imaging Research BACKGROUND: The non-local module has been primarily used in literature to capturing long-range dependencies. However, it suffers from prohibitive computational complexity and lacks the interactions among positions across the channels. METHODS: We present a deformed non-local neural network (DNL-Net) for medical image segmentation, which has two prominent components; deformed non-local module (DNL) and multi-scale feature fusion. The former optimizes the structure of the non-local block (NL), hence, reduces the problem of excessive computation and memory usage, significantly. The latter is derived from the attention mechanisms to fuse the features of different levels and improve the ability to exchange information across channels. In addition, we introduce a residual squeeze and excitation pyramid pooling (RSEP) module that is like spatial pyramid pooling to effectively resample the features at different scales and improve the network receptive field. RESULTS: The proposed method achieved 96.63% and 92.93% for Dice coefficient and mean intersection over union, respectively, on the intracranial blood vessel dataset. Also, DNL-Net attained 86.64%, 96.10%, and 98.37% for sensitivity, accuracy and area under receiver operation characteristic curve, respectively, on the DRIVE dataset. CONCLUSIONS: The overall performance of DNL-Net outperforms other current state-of-the-art vessel segmentation methods, which indicates that the proposed network is more suitable for blood vessel segmentation, and is of great clinical significance. BioMed Central 2022-06-06 /pmc/articles/PMC9169317/ /pubmed/35668351 http://dx.doi.org/10.1186/s12880-022-00836-z Text en © The Author(s) 2022 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Ni, Jiajia Wu, Jianhuang Elazab, Ahmed Tong, Jing Chen, Zhengming DNL-Net: deformed non-local neural network for blood vessel segmentation |
title | DNL-Net: deformed non-local neural network for blood vessel segmentation |
title_full | DNL-Net: deformed non-local neural network for blood vessel segmentation |
title_fullStr | DNL-Net: deformed non-local neural network for blood vessel segmentation |
title_full_unstemmed | DNL-Net: deformed non-local neural network for blood vessel segmentation |
title_short | DNL-Net: deformed non-local neural network for blood vessel segmentation |
title_sort | dnl-net: deformed non-local neural network for blood vessel segmentation |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9169317/ https://www.ncbi.nlm.nih.gov/pubmed/35668351 http://dx.doi.org/10.1186/s12880-022-00836-z |
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