The Impact of Interstitial Diseases Patterns on Lung CT Segmentation

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2021

Autores

Silva, F (Autor) Outra; Pereira, T (Autor) Outra; Morgado, J (Autor) Outra; Cunha, A (Autor) Outra; Oliveira, HP (Autor) FCUP

Ata de Conferência Internacional

Título: 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2021, Mexico, November 1-5, 2021

Páginas: 2856-2859

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 6 Citações

Outras Informações

ID Authenticus: P-00V-V1K

DOI: 10.1109/embc46164.2021.9630354

Abstract (EN): Lung segmentation represents a fundamental step in the development of computer-aided decision systems for the investigation of interstitial lung diseases. In a holistic lung analysis, eliminating background areas from Computed Tomography (CT) images is essential to avoid the inclusion of noise information and spend unnecessary computational resources on non-relevant data. However, the major challenge in this segmentation task relies on the ability of the models to deal with imaging manifestations associated with severe disease. Based on U-net, a general biomedical image segmentation architecture, we proposed a light-weight and faster architecture. In this 2D approach, experiments were conducted with a combination of two publicly available databases to improve the heterogeneity of the training data. Results showed that, when compared to the original U-net, the proposed architecture maintained performance levels, achieving 0.894 +/- 0.060, 4.493 +/- 0.633 and 4.457 +/- 0.628 for DSC, HD and HD-95 metrics, respectively, when using all patients from the ILD database for testing only, while allowing a more efficient computational usage. Quantitative and qualitative evaluations on the ability to cope with high-density lung patterns associated with severe disease were conducted, supporting the idea that more representative and diverse data is necessary to build robust and reliable segmentation tools.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4