CT evaluation of 2D and 3D holistic deep learning methods for the volumetric segmentation of airway lesions
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916353811677184 |
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| author | Bouzid, Amel Imene Hadj de Senneville, Baudouin Denis Baldacci, Fabien Desbarats, Pascal Berger, Patrick Benlala, Ilyes Dournes, Gaël |
| author_facet | Bouzid, Amel Imene Hadj de Senneville, Baudouin Denis Baldacci, Fabien Desbarats, Pascal Berger, Patrick Benlala, Ilyes Dournes, Gaël |
| contents | This research embarked on a comparative exploration of the holistic segmentation capabilities of Convolutional Neural Networks (CNNs) in both 2D and 3D formats, focusing on cystic fibrosis (CF) lesions. The study utilized data from two CF reference centers, covering five major CF structural changes. Initially, it compared the 2D and 3D models, highlighting the 3D model's superior capability in capturing complex features like mucus plugs and consolidations. To improve the 2D model's performance, a loss adapted to fine structures segmentation was implemented and evaluated, significantly enhancing its accuracy, though not surpassing the 3D model's performance. The models underwent further validation through external evaluation against pulmonary function tests (PFTs), confirming the robustness of the findings. Moreover, this study went beyond comparing metrics; it also included comprehensive assessments of the models' interpretability and reliability, providing valuable insights for their clinical application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08042 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CT evaluation of 2D and 3D holistic deep learning methods for the volumetric segmentation of airway lesions Bouzid, Amel Imene Hadj de Senneville, Baudouin Denis Baldacci, Fabien Desbarats, Pascal Berger, Patrick Benlala, Ilyes Dournes, Gaël Image and Video Processing Computer Vision and Pattern Recognition Machine Learning This research embarked on a comparative exploration of the holistic segmentation capabilities of Convolutional Neural Networks (CNNs) in both 2D and 3D formats, focusing on cystic fibrosis (CF) lesions. The study utilized data from two CF reference centers, covering five major CF structural changes. Initially, it compared the 2D and 3D models, highlighting the 3D model's superior capability in capturing complex features like mucus plugs and consolidations. To improve the 2D model's performance, a loss adapted to fine structures segmentation was implemented and evaluated, significantly enhancing its accuracy, though not surpassing the 3D model's performance. The models underwent further validation through external evaluation against pulmonary function tests (PFTs), confirming the robustness of the findings. Moreover, this study went beyond comparing metrics; it also included comprehensive assessments of the models' interpretability and reliability, providing valuable insights for their clinical application. |
| title | CT evaluation of 2D and 3D holistic deep learning methods for the volumetric segmentation of airway lesions |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.08042 |