Quality-based Regularization for Iterative Deep Image Segmentation

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

José Rebelo (Author) Other; Kelwin Fernandes (Author) Other; Jaime S. Cardoso (Author) FEUP

Conference proceedings International

Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Pages: 6734-6737

41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019

23 July 2019 through 27 July 2019

Indexing

ISI Web of Knowledge - 0 Citations

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Other information

Authenticus ID: P-00R-MMW

DOI: 10.1109/embc.2019.8857237

Abstract (EN): Traditional image segmentation algorithms operate by iteratively working over an image, as if refining a segmentation until a stopping criterion is met. Deep learning has replaced traditional approaches, achieving state-of-the-art performance in many problems, one of them being image segmentation. However, the concept of segmentation refinement is not present anymore, since usually the images are segmented in a single step. This work focuses on the refinement of image segmentations using deep convolutional neural networks, with the addition of a quality prediction output. The output from a state-of-the-art base segmenter is refined, simultaneously improving it and trying to predict its quality. We show that the quality concept can be used as a regularizer while training a network for direct segmentation refinement.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4