Synthesizing 3D Lung CT scans with Generative Adversarial Networks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Ferreira, A (Autor) Outra; Pereira, T (Autor) Outra; Silva, F (Autor) Outra; Vilares, AT (Autor) Outra; Silva, MC (Autor) Outra; Cunha, A (Autor) Outra; Oliveira, HP (Autor) FCUP

Ata de Conferência Internacional

Título: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022

Páginas: 2033-2036

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00X-G07

DOI: 10.1109/embc48229.2022.9871481

Abstract (EN): In the healthcare domain, datasets are often private and lack large amounts of samples, making it difficult to cope with the inherent patient data heterogeneity. As an attempt to mitigate data scarcity, generative models are being used due to their ability to produce new data, using a dataset as a reference. However, synthesis studies often rely on a 2D representation of data, a seriously limited form of information when it comes to lung computed tomography scans where, for example, pathologies like nodules can manifest anywhere in the organ. Here, we develop a 3D Progressive Growing Generative Adversarial Network capable of generating thoracic CT volumes at a resolution of 1283, and analyze the model outputs through a quantitative metric (3D Muli-Scale Structural Similarity) and a Visual Turing Test. Clinical relevance - This paper is a novel application of the 3D PGGAN model to synthesize CT lung scans. This preliminary study focuses on synthesizing the entire volume of the lung rather than just the lung nodules. The synthesized data represent an attempt to mitigate data scarcity which is one of the major limitations to create learning models with good generalization in healthcare.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4