An effective retinal blood vessel segmentation method using multi-scale line detection

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Deep Retinal Image Understanding (2016, methodology): “Classic methods for addressing the task of blood vessel segmentation involve hand crafted ﬁlters like line detectors [17,14] and vessel enhancement techniques [20,26,5].”
DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field (2016, methodology): “utilized a multi-scale line detection scheme to compute vessel segmentation [12].”
Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images (2015, methodology): “We selected nine unsupervised segmentation methods [5], [6], [11], [12], [15], [41]–[43].”
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images (2017, methodology): “Parameters for computing the unary features were initially ﬁxed to the values reported by the original references, which use the DRIVE training set [7], [42].”