Cargando…
Three-stage segmentation of lung region from CT images using deep neural networks
BACKGROUND: Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time consuming and the accuracy is dependent on radiologists’ experience. Automated...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280386/ https://www.ncbi.nlm.nih.gov/pubmed/34266391 http://dx.doi.org/10.1186/s12880-021-00640-1 |
_version_ | 1783722637279625216 |
---|---|
author | Osadebey, Michael Andersen, Hilde K. Waaler, Dag Fossaa, Kristian Martinsen, Anne C. T. Pedersen, Marius |
author_facet | Osadebey, Michael Andersen, Hilde K. Waaler, Dag Fossaa, Kristian Martinsen, Anne C. T. Pedersen, Marius |
author_sort | Osadebey, Michael |
collection | PubMed |
description | BACKGROUND: Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time consuming and the accuracy is dependent on radiologists’ experience. Automated methods are relatively fast and reproducible with potential to facilitate physician interpretation of images. However, these benefits are possible only after overcoming several challenges. The traditional methods that are formulated as a three-stage segmentation demonstrate promising results on normal CT data but perform poorly in the presence of pathological features and variations in image quality attributes. The implementation of deep learning methods that can demonstrate superior performance over traditional methods is dependent on the quantity, quality, cost and the time it takes to generate training data. Thus, efficient and clinically relevant automated segmentation method is desired for the diagnosis of respiratory diseases. METHODS: We implement each of the three stages of traditional methods using deep learning methods trained on five different configurations of training data with ground truths obtained from the 3D Image Reconstruction for Comparison of Algorithm Database (3DIRCAD) and the Interstitial Lung Diseases (ILD) database. The data was augmented with the Lung Image Database Consortium (LIDC-IDRI) image collection and a realistic phantom. A convolutional neural network (CNN) at the preprocessing stage classifies the input into lung and none lung regions. The processing stage was implemented using a CNN-based U-net while the postprocessing stage utilize another U-net and CNN for contour refinement and filtering out false positives, respectively. RESULTS: The performance of the proposed method was evaluated on 1230 and 1100 CT slices from the 3DIRCAD and ILD databases. We investigate the performance of the proposed method on five configurations of training data and three configurations of the segmentation system; three-stage segmentation and three-stage segmentation without a CNN classifier and contrast enhancement, respectively. The Dice-score recorded by the proposed method range from 0.76 to 0.95. CONCLUSION: The clinical relevance and segmentation accuracy of deep learning models can improve though deep learning-based three-stage segmentation, image quality evaluation and enhancement as well as augmenting the training data with large volume of cheap and quality training data. We propose a new and novel deep learning-based method of contour refinement. |
format | Online Article Text |
id | pubmed-8280386 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82803862021-07-16 Three-stage segmentation of lung region from CT images using deep neural networks Osadebey, Michael Andersen, Hilde K. Waaler, Dag Fossaa, Kristian Martinsen, Anne C. T. Pedersen, Marius BMC Med Imaging Research Article BACKGROUND: Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time consuming and the accuracy is dependent on radiologists’ experience. Automated methods are relatively fast and reproducible with potential to facilitate physician interpretation of images. However, these benefits are possible only after overcoming several challenges. The traditional methods that are formulated as a three-stage segmentation demonstrate promising results on normal CT data but perform poorly in the presence of pathological features and variations in image quality attributes. The implementation of deep learning methods that can demonstrate superior performance over traditional methods is dependent on the quantity, quality, cost and the time it takes to generate training data. Thus, efficient and clinically relevant automated segmentation method is desired for the diagnosis of respiratory diseases. METHODS: We implement each of the three stages of traditional methods using deep learning methods trained on five different configurations of training data with ground truths obtained from the 3D Image Reconstruction for Comparison of Algorithm Database (3DIRCAD) and the Interstitial Lung Diseases (ILD) database. The data was augmented with the Lung Image Database Consortium (LIDC-IDRI) image collection and a realistic phantom. A convolutional neural network (CNN) at the preprocessing stage classifies the input into lung and none lung regions. The processing stage was implemented using a CNN-based U-net while the postprocessing stage utilize another U-net and CNN for contour refinement and filtering out false positives, respectively. RESULTS: The performance of the proposed method was evaluated on 1230 and 1100 CT slices from the 3DIRCAD and ILD databases. We investigate the performance of the proposed method on five configurations of training data and three configurations of the segmentation system; three-stage segmentation and three-stage segmentation without a CNN classifier and contrast enhancement, respectively. The Dice-score recorded by the proposed method range from 0.76 to 0.95. CONCLUSION: The clinical relevance and segmentation accuracy of deep learning models can improve though deep learning-based three-stage segmentation, image quality evaluation and enhancement as well as augmenting the training data with large volume of cheap and quality training data. We propose a new and novel deep learning-based method of contour refinement. BioMed Central 2021-07-15 /pmc/articles/PMC8280386/ /pubmed/34266391 http://dx.doi.org/10.1186/s12880-021-00640-1 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/) . 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 Article Osadebey, Michael Andersen, Hilde K. Waaler, Dag Fossaa, Kristian Martinsen, Anne C. T. Pedersen, Marius Three-stage segmentation of lung region from CT images using deep neural networks |
title | Three-stage segmentation of lung region from CT images using deep neural networks |
title_full | Three-stage segmentation of lung region from CT images using deep neural networks |
title_fullStr | Three-stage segmentation of lung region from CT images using deep neural networks |
title_full_unstemmed | Three-stage segmentation of lung region from CT images using deep neural networks |
title_short | Three-stage segmentation of lung region from CT images using deep neural networks |
title_sort | three-stage segmentation of lung region from ct images using deep neural networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280386/ https://www.ncbi.nlm.nih.gov/pubmed/34266391 http://dx.doi.org/10.1186/s12880-021-00640-1 |
work_keys_str_mv | AT osadebeymichael threestagesegmentationoflungregionfromctimagesusingdeepneuralnetworks AT andersenhildek threestagesegmentationoflungregionfromctimagesusingdeepneuralnetworks AT waalerdag threestagesegmentationoflungregionfromctimagesusingdeepneuralnetworks AT fossaakristian threestagesegmentationoflungregionfromctimagesusingdeepneuralnetworks AT martinsenannect threestagesegmentationoflungregionfromctimagesusingdeepneuralnetworks AT pedersenmarius threestagesegmentationoflungregionfromctimagesusingdeepneuralnetworks |