Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores

By Jiong Zhang, B. Dashtbozorg, Erik J. Bekkers, Josien P. W. Pluim, Remco Duits, B. T. H. Romeny. IEEE Transactions on Medical Imaging, 2016

407 citations recorded by the source corpus.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/14250467826cd2888989d3cc2e2638ebd52dffd6

How researchers see this paper

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

In Ophthalmology

1. Kwang-Ting Cheng
2. Xin Yang
3. Zengqiang Yan
4. Gary G. Yen
5. Yao Zhou
6. Zhang Yi

In Radiology, Nuclear Medicine and Imaging

1. Kwang-Ting Cheng
2. Xin Yang
3. Zengqiang Yan
4. Arvid Lundervold
5. Gary G. Yen
6. Yao Zhou
7. Zhang Yi

Citations in the corpus

• A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation (2019, methodology): “[17] transformed fundus images into the lifted domain by wavelet transformation, and used a multi-scale second-order Gaussian filter for vessel segmentation.”
• Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation (2018, methodology): “[17] proposed to solve the vessel segmentation problem by transforming a 2D image into the lifted domain via wavelet transformation, and used a multi-scale”