Long-Range Decoder Skip Connections: Exploiting Multi-Context Information for Cardiac Image Segmentation

Author(s)

Gutierrez-Castilla, Nicolas ; Torres, Ricardo da S. ; Falcao, Alexandre X. ; Kozerke, Sebastian ; Schwitter, Jurg ; Masci, Pier-Giorgio ; Montoya-Zegarra, Javier A. ; IEEE

• Document type: Journal article
• Source: 2019 32ND SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 8-pg., 2019-01-01.

Abstract

The heart is one of the most important organs in our body and many critical diseases are associated with its malfunctioning. To assess the risk for heart diseases, Magnetic Resonance Imaging (MRI) has become the golden standard imaging technique, as it provides to the clinicians stacks of images for analyzing the heart structures, such as the ventricles, and thus to make a diagnosis of the patient's health. The problem is that examination of these stacks, often based on the delineation of heart structures, is tedious and error prone due to inter- and intra-variability among manual delineations. For this reason, the investigation of fully automated methods to support heart segmentation is paramount. Most of the successful methods proposed to solve this problem are based on deep-learning solutions. Especially, encoder-decoder architectures, such as the U-Net [1], have demonstrated to be very effective architectures for medical image segmentation. In this paper, we propose to use long-range skip connections on the decoder-part to incorporate multi-context information onto the predicted segmentation masks and also to improve the generalization of the models. In addition, our method obtains smoother segmentations through the combination of feature maps from different stages onto the final prediction layer. We evaluate our approach in the ACDC [2] and LVSC [3] heart segmentation challenges. Experiments performed on both datasets demonstrate that our approach leads to an improvement on both the total Dice score and the Ejection Fraction Correlation, when combined with state-of-the-art encoder-decoder architectures. (AU)

• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 16/50250-1 - The secret of playing football: Brazil versus the Netherlands
• Grantee: Sergio Augusto Cunha
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 17/20945-0 - Multi-user equipment approved in great 16/50250-1: local positioning system
• Grantee: Sergio Augusto Cunha
• Support Opportunities: Multi-user Equipment Program
• FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
• Grantee: Nelson Luis Saldanha da Fonseca
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
• Grantee: Leonor Patricia Cerdeira Morellato
• Support Opportunities: Research Program on Global Climate Change - University-Industry Cooperative Research (PITE)