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Papers/Deep Vessel Segmentation By Learning Graphical Connectivity

Deep Vessel Segmentation By Learning Graphical Connectivity

Seung Yeon Shin, Soochahn Lee, Il Dong Yun, Kyoung Mu Lee

2018-06-06Retinal Vessel SegmentationSegmentation
PaperPDFCode(official)

Abstract

We propose a novel deep-learning-based system for vessel segmentation. Existing methods using CNNs have mostly relied on local appearances learned on the regular image grid, without considering the graphical structure of vessel shape. To address this, we incorporate a graph convolutional network into a unified CNN architecture, where the final segmentation is inferred by combining the different types of features. The proposed method can be applied to expand any type of CNN-based vessel segmentation method to enhance the performance. Experiments show that the proposed method outperforms the current state-of-the-art methods on two retinal image datasets as well as a coronary artery X-ray angiography dataset.

Results

TaskDatasetMetricValueModel
Medical Image SegmentationHRFAUC0.9838VGN
Medical Image SegmentationHRFF1 score0.8151VGN
Medical Image SegmentationCHASE_DB1AUC0.983VGN
Medical Image SegmentationCHASE_DB1F1 score0.8034VGN
Medical Image SegmentationSTAREAUC0.9877VGN
Medical Image SegmentationSTAREF1 score0.8429VGN
Medical Image SegmentationDRIVEAUC0.9802VGN
Medical Image SegmentationDRIVEF1 score0.8263VGN
Retinal Vessel SegmentationHRFAUC0.9838VGN
Retinal Vessel SegmentationHRFF1 score0.8151VGN
Retinal Vessel SegmentationCHASE_DB1AUC0.983VGN
Retinal Vessel SegmentationCHASE_DB1F1 score0.8034VGN
Retinal Vessel SegmentationSTAREAUC0.9877VGN
Retinal Vessel SegmentationSTAREF1 score0.8429VGN
Retinal Vessel SegmentationDRIVEAUC0.9802VGN
Retinal Vessel SegmentationDRIVEF1 score0.8263VGN

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