A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation

By Zengqiang Yan, Xin Yang, Kwang-Ting Cheng. IEEE journal of biomedical and health informatics, 2019

305 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b14cf00b44d9bdd9a76148bff9afa9ed453876ce

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Gary G. Yen
2. Yao Zhou
3. Zhang Yi
4. Bing Xue
5. Mengjie Zhang
6. Yanan Sun

In Ophthalmology

1. Gary G. Yen
2. Yao Zhou
3. Zhang Yi

In Radiology, Nuclear Medicine and Imaging

1. Gary G. Yen
2. Yao Zhou
3. Zhang Yi

Citations in the corpus

• Evolutionary Compression of Deep Neural Networks for Biomedical Image Segmentation (2020, background): “This trend is mainly attributed to the paradigm that transferred from traditional image processing and machine learning to deep learning [31], [32].”