A Deep Learning Design for Improving Topology Coherence in Blood Vessel Segmentation

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Ricardo J. Araújo (Autor) Outra; Jaime S. Cardoso (Autor) FEUP; Hélder P. Oliveira (Autor) FCUP

Ata de Conferência Internacional

Título: MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2019, PT I

Páginas: 93-101

10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

Shenzhen, PEOPLES R CHINA, OCT 13-17, 2019

Indexação

ISI Web of Knowledge - 27 Citações

Scopus - 31 Citações

Outras Informações

ID Authenticus: P-00R-CJC

DOI: 10.1007/978-3-030-32239-7_11

Abstract (EN): The segmentation of blood vessels in medical images has been heavily studied, given its impact in several clinical practices. Deep Learning methods have been applied to supervised segmentation of blood vessels, mainly the retinal ones due to the availability of manual annotations. Despite their success, they typically minimize the Binary Cross Entropy loss, which does not penalize topological mistakes. These errors are relevant in graph-like structures such as blood vessel trees, as a missing segment or an inadequate merging or splitting of branches, may severely change the topology of the network and put at risk the extraction of vessel pathways and their characterization. In this paper, we propose an end-to-end network design comprising a cascade of a typical segmentation network and a Variational Auto-Encoder which, by learning a rich but compact latent space, is able to correct many topological incoherences. Our experiments in three of the most commonly used retinal databases, DRIVE, STARE, and CHASEDB1, show that the proposed model effectively learns representations inducing better segmentations in terms of topology, without hurting the usual pixel-wise metrics.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9