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
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.