Implementation of Automatic Segmentation Framework as Preprocessing Step for Radiomics Analysis of Lung Anatomical Districts
<b>Background:</b> The advent of artificial intelligence has significantly impacted radiology, with radiomics emerging as a transformative approach that extracts quantitative data from medical images to improve diagnostic and therapeutic accuracy. This study aimed to enhance the radiomic...
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MDPI AG
2024-12-01
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| author | Alessandro Stefano Fabiano Bini Nicolò Lauciello Giovanni Pasini Franco Marinozzi Giorgio Russo |
| author_facet | Alessandro Stefano Fabiano Bini Nicolò Lauciello Giovanni Pasini Franco Marinozzi Giorgio Russo |
| author_sort | Alessandro Stefano |
| collection | DOAJ |
| description | <b>Background:</b> The advent of artificial intelligence has significantly impacted radiology, with radiomics emerging as a transformative approach that extracts quantitative data from medical images to improve diagnostic and therapeutic accuracy. This study aimed to enhance the radiomic workflow by applying deep learning, through transfer learning, for the automatic segmentation of lung regions in computed tomography scans as a preprocessing step. <b>Methods:</b> Leveraging a pipeline articulated in (i) patient-based data splitting, (ii) intensity normalization, (iii) voxel resampling, (iv) bed removal, (v) contrast enhancement and (vi) model training, a DeepLabV3+ convolutional neural network (CNN) was fine tuned to perform whole-lung-region segmentation. <b>Results:</b> The trained model achieved high accuracy, Dice coefficient (0.97) and BF (93.06%) scores, and it effectively preserved lung region areas and removed confounding anatomical regions such as the heart and the spine. <b>Conclusions:</b> This study introduces a deep learning framework for the automatic segmentation of lung regions in CT images, leveraging an articulated pipeline and demonstrating excellent performance of the model, effectively isolating lung regions while excluding confounding anatomical structures. Ultimately, this work paves the way for more efficient, automated preprocessing tools in lung cancer detection, with potential to significantly improve clinical decision making and patient outcomes. |
| format | Article |
| id | doaj-art-384e3cd0124449998f8df742ded98d7b |
| institution | DOAJ |
| issn | 2673-7426 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioMedInformatics |
| spelling | doaj-art-384e3cd0124449998f8df742ded98d7b2025-08-20T02:55:36ZengMDPI AGBioMedInformatics2673-74262024-12-01442309232010.3390/biomedinformatics4040125Implementation of Automatic Segmentation Framework as Preprocessing Step for Radiomics Analysis of Lung Anatomical DistrictsAlessandro Stefano0Fabiano Bini1Nicolò Lauciello2Giovanni Pasini3Franco Marinozzi4Giorgio Russo5Institute of Bioimaging and Complex Biological Systems—National Research Council (IBSBC-CNR), Contrada Pietrapollastra-Pisciotto, Cefalù, 90015 Palermo, ItalyDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, ItalyDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, ItalyInstitute of Bioimaging and Complex Biological Systems—National Research Council (IBSBC-CNR), Contrada Pietrapollastra-Pisciotto, Cefalù, 90015 Palermo, ItalyDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, ItalyInstitute of Bioimaging and Complex Biological Systems—National Research Council (IBSBC-CNR), Contrada Pietrapollastra-Pisciotto, Cefalù, 90015 Palermo, Italy<b>Background:</b> The advent of artificial intelligence has significantly impacted radiology, with radiomics emerging as a transformative approach that extracts quantitative data from medical images to improve diagnostic and therapeutic accuracy. This study aimed to enhance the radiomic workflow by applying deep learning, through transfer learning, for the automatic segmentation of lung regions in computed tomography scans as a preprocessing step. <b>Methods:</b> Leveraging a pipeline articulated in (i) patient-based data splitting, (ii) intensity normalization, (iii) voxel resampling, (iv) bed removal, (v) contrast enhancement and (vi) model training, a DeepLabV3+ convolutional neural network (CNN) was fine tuned to perform whole-lung-region segmentation. <b>Results:</b> The trained model achieved high accuracy, Dice coefficient (0.97) and BF (93.06%) scores, and it effectively preserved lung region areas and removed confounding anatomical regions such as the heart and the spine. <b>Conclusions:</b> This study introduces a deep learning framework for the automatic segmentation of lung regions in CT images, leveraging an articulated pipeline and demonstrating excellent performance of the model, effectively isolating lung regions while excluding confounding anatomical structures. Ultimately, this work paves the way for more efficient, automated preprocessing tools in lung cancer detection, with potential to significantly improve clinical decision making and patient outcomes.https://www.mdpi.com/2673-7426/4/4/125radiomicsdeep learningsegmentation |
| spellingShingle | Alessandro Stefano Fabiano Bini Nicolò Lauciello Giovanni Pasini Franco Marinozzi Giorgio Russo Implementation of Automatic Segmentation Framework as Preprocessing Step for Radiomics Analysis of Lung Anatomical Districts BioMedInformatics radiomics deep learning segmentation |
| title | Implementation of Automatic Segmentation Framework as Preprocessing Step for Radiomics Analysis of Lung Anatomical Districts |
| title_full | Implementation of Automatic Segmentation Framework as Preprocessing Step for Radiomics Analysis of Lung Anatomical Districts |
| title_fullStr | Implementation of Automatic Segmentation Framework as Preprocessing Step for Radiomics Analysis of Lung Anatomical Districts |
| title_full_unstemmed | Implementation of Automatic Segmentation Framework as Preprocessing Step for Radiomics Analysis of Lung Anatomical Districts |
| title_short | Implementation of Automatic Segmentation Framework as Preprocessing Step for Radiomics Analysis of Lung Anatomical Districts |
| title_sort | implementation of automatic segmentation framework as preprocessing step for radiomics analysis of lung anatomical districts |
| topic | radiomics deep learning segmentation |
| url | https://www.mdpi.com/2673-7426/4/4/125 |
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