Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 23, Issue 4, Pages 1427-1436Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2018.2872813
Keywords
Deep learning; vessel segmentation; imbalance problem; retinal image analysis
Categories
Funding
- National Key Research and Development Program of China [2017YFA0700402]
- National Natural Science Foundation of China [61872417, 2017010201010111]
- Program for Huazhong University of Science and Technology Academic Frontier Youth Team
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Automatic retinal vessel segmentation is a fundamental step in the diagnosis of eye-related diseases, in which both thick vessels and thin vessels are important features for symptom detection. All existing deep learning models attempt to segment both types of vessels simultaneously by using a unified pixel-wise loss that treats all vessel pixels with equal importance. Due to the highly imbalanced ratio between thick vessels and thin vessels (namely the majority of vessel pixels belong to thick vessels), the pixel-wise loss would be dominantly guided by thick vessels and relatively little influence comes from thin vessels, often leading to low segmentation accuracy for thin vessels. To address the imbalance problem, in this paper, we explore to segment thick vessels and thin vessels separately by proposing a three-stage deep learning model. The vessel segmentation task is divided into three stages, namely thick vessel segmentation, thin vessel segmentation, and vessel fusion. As better discriminative features could be learned for separate segmentation of thick vessels and thin vessels, this process minimizes the negative influence caused by their highly imbalanced ratio. The final vessel fusion stage refines the results by further identifying nonvessel pixels and improving the overall vessel thickness consistency. The experiments on public datasets DRIVE, STARE, and CHASE_DB1 clearly demonstrate that the proposed three-stage deep learning model outperforms the current state-of-the-art vessel segmentation methods.
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