CT Evaluation of 2D and 3D Holistic Deep Learning Methods for the Volumetric Segmentation of Airway Lesions
DENIS DE SENNEVILLE, Baudouin
Institut de Mathématiques de Bordeaux [IMB]
Modélisation Mathématique pour l'Oncologie [MONC]
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Institut de Mathématiques de Bordeaux [IMB]
Modélisation Mathématique pour l'Oncologie [MONC]
DENIS DE SENNEVILLE, Baudouin
Institut de Mathématiques de Bordeaux [IMB]
Modélisation Mathématique pour l'Oncologie [MONC]
< Leer menos
Institut de Mathématiques de Bordeaux [IMB]
Modélisation Mathématique pour l'Oncologie [MONC]
Idioma
EN
Communication dans un congrès
Este ítem está publicado en
ISBI 2024 - 21st IEEE International Symposium on Biomedical Imaging, 2024-05-27, Athènes. 2024p. 1-5
IEEE
Resumen en inglés
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 ...Leer más >
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.< Leer menos
Palabras clave en inglés
Image and Video Processing (eess.IV)
Computer Vision and Pattern Recognition (cs.CV)
Machine Learning (cs.LG)
FOS: Electrical engineering
electronic engineering
information engineering
FOS: Computer and information sciences
Centros de investigación