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Grayscale medical image segmentation method based on 2D&3D object detection with deep learning
BACKGROUND: Grayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Model-driven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extraction. However, model-driven methods like thresholding...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8883636/ https://www.ncbi.nlm.nih.gov/pubmed/35220942 http://dx.doi.org/10.1186/s12880-022-00760-2 |
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author | Ge, Yunfei Zhang, Qing Sun, Yuantao Shen, Yidong Wang, Xijiong |
author_facet | Ge, Yunfei Zhang, Qing Sun, Yuantao Shen, Yidong Wang, Xijiong |
author_sort | Ge, Yunfei |
collection | PubMed |
description | BACKGROUND: Grayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Model-driven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extraction. However, model-driven methods like thresholding usually suffer from wrong segmentation and noises regions because different grayscale images have distinct intensity distribution property thus pre-processing is always demanded. While data-driven methods with deep learning like encoder-decoder networks always are always accompanied by complex architectures which require amounts of training data. METHODS: Combining thresholding method and deep learning, this paper presents a novel method by using 2D&3D object detection technologies. First, interest regions contain segmented object are determined with fine-tuning 2D object detection network. Then, pixels in cropped images are turned as point cloud according to their positions and grayscale values. Finally, 3D object detection network is applied to obtain bounding boxes with target points and boxes’ bottoms and tops represent thresholding values for segmentation. After projecting to 2D images, these target points could composite the segmented object. RESULTS: Three groups of grayscale medical images are used to evaluate the proposed image segmentation method. We obtain the IoU (DSC) scores of 0.92 (0.96), 0.88 (0.94) and 0.94 (0.94) for segmentation accuracy on different datasets respectively. Also, compared with five state of the arts and clinically performed well models, our method achieves higher scores and better performance. CONCLUSIONS: The prominent segmentation results demonstrate that the built method based on 2D&3D object detection with deep learning is workable and promising for segmentation task of grayscale medical images. |
format | Online Article Text |
id | pubmed-8883636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88836362022-03-07 Grayscale medical image segmentation method based on 2D&3D object detection with deep learning Ge, Yunfei Zhang, Qing Sun, Yuantao Shen, Yidong Wang, Xijiong BMC Med Imaging Research BACKGROUND: Grayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Model-driven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extraction. However, model-driven methods like thresholding usually suffer from wrong segmentation and noises regions because different grayscale images have distinct intensity distribution property thus pre-processing is always demanded. While data-driven methods with deep learning like encoder-decoder networks always are always accompanied by complex architectures which require amounts of training data. METHODS: Combining thresholding method and deep learning, this paper presents a novel method by using 2D&3D object detection technologies. First, interest regions contain segmented object are determined with fine-tuning 2D object detection network. Then, pixels in cropped images are turned as point cloud according to their positions and grayscale values. Finally, 3D object detection network is applied to obtain bounding boxes with target points and boxes’ bottoms and tops represent thresholding values for segmentation. After projecting to 2D images, these target points could composite the segmented object. RESULTS: Three groups of grayscale medical images are used to evaluate the proposed image segmentation method. We obtain the IoU (DSC) scores of 0.92 (0.96), 0.88 (0.94) and 0.94 (0.94) for segmentation accuracy on different datasets respectively. Also, compared with five state of the arts and clinically performed well models, our method achieves higher scores and better performance. CONCLUSIONS: The prominent segmentation results demonstrate that the built method based on 2D&3D object detection with deep learning is workable and promising for segmentation task of grayscale medical images. BioMed Central 2022-02-27 /pmc/articles/PMC8883636/ /pubmed/35220942 http://dx.doi.org/10.1186/s12880-022-00760-2 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 Ge, Yunfei Zhang, Qing Sun, Yuantao Shen, Yidong Wang, Xijiong Grayscale medical image segmentation method based on 2D&3D object detection with deep learning |
title | Grayscale medical image segmentation method based on 2D&3D object detection with deep learning |
title_full | Grayscale medical image segmentation method based on 2D&3D object detection with deep learning |
title_fullStr | Grayscale medical image segmentation method based on 2D&3D object detection with deep learning |
title_full_unstemmed | Grayscale medical image segmentation method based on 2D&3D object detection with deep learning |
title_short | Grayscale medical image segmentation method based on 2D&3D object detection with deep learning |
title_sort | grayscale medical image segmentation method based on 2d&3d object detection with deep learning |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8883636/ https://www.ncbi.nlm.nih.gov/pubmed/35220942 http://dx.doi.org/10.1186/s12880-022-00760-2 |
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