Synthesizing 3D Lung CT scans with Generative Adversarial Networks

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

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

Conference proceedings International

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

Pages: 2033-2036

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Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4