CT evaluation of 2D and 3D holistic deep learning methods for the volumetric segmentation of airway lesions

Amel Imene Hadj Bouzid; Baudouin Denis de Senneville; Fabien Baldacci; Pascal Desbarats; Patrick Berger; Ilyes Benlala; Gaël Dournes

arXiv · 2403.08042

CT evaluation of 2D and 3D holistic deep learning methods for the volumetric segmentation of airway lesions

Amel Imene Hadj Bouzid·Baudouin Denis de Senneville·Fabien Baldacci·Pascal Desbarats·Patrick Berger·Ilyes Benlala·Gaël Dournes

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Abstract

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.

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eess.IV cs.CV cs.LG

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Amel Imene Hadj Bouzid, Baudouin Denis de Senneville, Fabien Baldacci, Pascal Desbarats, Patrick Berger, Ilyes Benlala, Gaël Dournes. 2024-03-12. CT evaluation of 2D and 3D holistic deep learning methods for the volumetric segmentation of airway lesions. https://arxiv.org/abs/2403.08042

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