End-to-End Adversarial Retinal Image Synthesis

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

Pedro Costa (Autor) Outra; Adrian Galdran (Autor) Outra; Maria Ines Meyer (Autor) Outra; Meindert Niemeijer (Autor) Outra; Michael Abràmoff (Autor) Outra; Ana Maria Mendonça (Autor) FEUP; Aurélio Campilho (Autor) FEUP

Revista

Título: IEEE Transactions on Medical Imaging

Vol. 37

Páginas: 781-791

ISSN: 0278-0062

Editora: IEEE

Indexação

ISI Web of Knowledge - 187 Citações

Scopus - 217 Citações

INSPEC

Outras Informações

ID Authenticus: P-00N-22X

DOI: 10.1109/tmi.2017.2759102

Abstract (EN): In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11