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Enhancing semantic segmentation in chest X-ray images through image preprocessing: ps-KDE for pixel-wise substitution by kernel density estimation
In medical imaging, the integration of deep-learning-based semantic segmentation algorithms with preprocessing techniques can reduce the need for human annotation and advance disease classification. Among established preprocessing techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE)...
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| Published in: | PloS one 2024-06, Vol.19 (6), p.e0299623 |
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| creator | Wang, Yuanchen Guo, Yujie Wang, Ziqi Yu, Linzi Yan, Yujie Gu, Zifan |
| description | In medical imaging, the integration of deep-learning-based semantic segmentation algorithms with preprocessing techniques can reduce the need for human annotation and advance disease classification. Among established preprocessing techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) has demonstrated efficacy in improving segmentation algorithms across various modalities, such as X-rays and CT. However, there remains a demand for improved contrast enhancement methods considering the heterogeneity of datasets and the various contrasts across different anatomic structures.
This study proposes a novel preprocessing technique, ps-KDE, to investigate its impact on deep learning algorithms to segment major organs in posterior-anterior chest X-rays. Ps-KDE augments image contrast by substituting pixel values based on their normalized frequency across all images. We evaluate our approach on a U-Net architecture with ResNet34 backbone pre-trained on ImageNet. Five separate models are trained to segment the heart, left lung, right lung, left clavicle, and right clavicle.
The model trained to segment the left lung using ps-KDE achieved a Dice score of 0.780 (SD = 0.13), while that of trained on CLAHE achieved a Dice score of 0.717 (SD = 0.19), p |
| doi_str_mv | 10.1371/journal.pone.0299623 |
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This study proposes a novel preprocessing technique, ps-KDE, to investigate its impact on deep learning algorithms to segment major organs in posterior-anterior chest X-rays. Ps-KDE augments image contrast by substituting pixel values based on their normalized frequency across all images. We evaluate our approach on a U-Net architecture with ResNet34 backbone pre-trained on ImageNet. Five separate models are trained to segment the heart, left lung, right lung, left clavicle, and right clavicle.
The model trained to segment the left lung using ps-KDE achieved a Dice score of 0.780 (SD = 0.13), while that of trained on CLAHE achieved a Dice score of 0.717 (SD = 0.19), p<0.01. ps-KDE also appears to be more robust as CLAHE-based models misclassified right lungs in select test images for the left lung model. The algorithm for performing ps-KDE is available at https://github.com/wyc79/ps-KDE.
Our results suggest that ps-KDE offers advantages over current preprocessing techniques when segmenting certain lung regions. This could be beneficial in subsequent analyses such as disease classification and risk stratification.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0299623</identifier><identifier>PMID: 38913621</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Biology and Life Sciences ; Chest ; Classification ; Clavicle ; Clavicle - diagnostic imaging ; Computed tomography ; Computer and Information Sciences ; Datasets ; Deep Learning ; Engineering and Technology ; Heterogeneity ; Histograms ; Humans ; Image contrast ; Image enhancement ; Image processing ; Image Processing, Computer-Assisted - methods ; Image segmentation ; Lung - diagnostic imaging ; Lungs ; Machine learning ; Magnetic resonance imaging ; Medical imaging ; Medicine and Health Sciences ; Methods ; Physical Sciences ; Pixels ; Preprocessing ; Radiography, Thoracic - methods ; Research and Analysis Methods ; Semantic segmentation ; Semantics ; Tomography, X-Ray Computed - methods ; X-rays</subject><ispartof>PloS one, 2024-06, Vol.19 (6), p.e0299623</ispartof><rights>Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 Wang et al 2024 Wang et al</rights><rights>2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c406t-a1974dd77cfddaaa2d949dd1dc7e88dfab24b9f72147374a000852738068884c3</cites><orcidid>0000-0002-8024-2629 ; 0000-0002-9455-8106</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3071892830?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3071892830?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,38516,43895,44590,53791,53793,74412,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38913621$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Shaikh, Asadullah</contributor><creatorcontrib>Wang, Yuanchen</creatorcontrib><creatorcontrib>Guo, Yujie</creatorcontrib><creatorcontrib>Wang, Ziqi</creatorcontrib><creatorcontrib>Yu, Linzi</creatorcontrib><creatorcontrib>Yan, Yujie</creatorcontrib><creatorcontrib>Gu, Zifan</creatorcontrib><title>Enhancing semantic segmentation in chest X-ray images through image preprocessing: ps-KDE for pixel-wise substitution by kernel density estimation</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>In medical imaging, the integration of deep-learning-based semantic segmentation algorithms with preprocessing techniques can reduce the need for human annotation and advance disease classification. Among established preprocessing techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) has demonstrated efficacy in improving segmentation algorithms across various modalities, such as X-rays and CT. However, there remains a demand for improved contrast enhancement methods considering the heterogeneity of datasets and the various contrasts across different anatomic structures.
This study proposes a novel preprocessing technique, ps-KDE, to investigate its impact on deep learning algorithms to segment major organs in posterior-anterior chest X-rays. Ps-KDE augments image contrast by substituting pixel values based on their normalized frequency across all images. We evaluate our approach on a U-Net architecture with ResNet34 backbone pre-trained on ImageNet. Five separate models are trained to segment the heart, left lung, right lung, left clavicle, and right clavicle.
The model trained to segment the left lung using ps-KDE achieved a Dice score of 0.780 (SD = 0.13), while that of trained on CLAHE achieved a Dice score of 0.717 (SD = 0.19), p<0.01. ps-KDE also appears to be more robust as CLAHE-based models misclassified right lungs in select test images for the left lung model. The algorithm for performing ps-KDE is available at https://github.com/wyc79/ps-KDE.
Our results suggest that ps-KDE offers advantages over current preprocessing techniques when segmenting certain lung regions. This could be beneficial in subsequent analyses such as disease classification and risk stratification.</description><subject>Algorithms</subject><subject>Biology and Life Sciences</subject><subject>Chest</subject><subject>Classification</subject><subject>Clavicle</subject><subject>Clavicle - diagnostic imaging</subject><subject>Computed tomography</subject><subject>Computer and Information Sciences</subject><subject>Datasets</subject><subject>Deep Learning</subject><subject>Engineering and Technology</subject><subject>Heterogeneity</subject><subject>Histograms</subject><subject>Humans</subject><subject>Image contrast</subject><subject>Image enhancement</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image segmentation</subject><subject>Lung - diagnostic imaging</subject><subject>Lungs</subject><subject>Machine learning</subject><subject>Magnetic resonance imaging</subject><subject>Medical imaging</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Physical Sciences</subject><subject>Pixels</subject><subject>Preprocessing</subject><subject>Radiography, Thoracic - 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Among established preprocessing techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) has demonstrated efficacy in improving segmentation algorithms across various modalities, such as X-rays and CT. However, there remains a demand for improved contrast enhancement methods considering the heterogeneity of datasets and the various contrasts across different anatomic structures.
This study proposes a novel preprocessing technique, ps-KDE, to investigate its impact on deep learning algorithms to segment major organs in posterior-anterior chest X-rays. Ps-KDE augments image contrast by substituting pixel values based on their normalized frequency across all images. We evaluate our approach on a U-Net architecture with ResNet34 backbone pre-trained on ImageNet. Five separate models are trained to segment the heart, left lung, right lung, left clavicle, and right clavicle.
The model trained to segment the left lung using ps-KDE achieved a Dice score of 0.780 (SD = 0.13), while that of trained on CLAHE achieved a Dice score of 0.717 (SD = 0.19), p<0.01. ps-KDE also appears to be more robust as CLAHE-based models misclassified right lungs in select test images for the left lung model. The algorithm for performing ps-KDE is available at https://github.com/wyc79/ps-KDE.
Our results suggest that ps-KDE offers advantages over current preprocessing techniques when segmenting certain lung regions. This could be beneficial in subsequent analyses such as disease classification and risk stratification.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>38913621</pmid><doi>10.1371/journal.pone.0299623</doi><orcidid>https://orcid.org/0000-0002-8024-2629</orcidid><orcidid>https://orcid.org/0000-0002-9455-8106</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Biology and Life Sciences Chest Classification Clavicle Clavicle - diagnostic imaging Computed tomography Computer and Information Sciences Datasets Deep Learning Engineering and Technology Heterogeneity Histograms Humans Image contrast Image enhancement Image processing Image Processing, Computer-Assisted - methods Image segmentation Lung - diagnostic imaging Lungs Machine learning Magnetic resonance imaging Medical imaging Medicine and Health Sciences Methods Physical Sciences Pixels Preprocessing Radiography, Thoracic - methods Research and Analysis Methods Semantic segmentation Semantics Tomography, X-Ray Computed - methods X-rays |
| title | Enhancing semantic segmentation in chest X-ray images through image preprocessing: ps-KDE for pixel-wise substitution by kernel density estimation |
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