02 Feb 21. SINAPSE Image of the Month
Edinburgh Imaging is delighted to see that an image courtesy of Ylenia Giarratano, Dr Tom MacGillivray & Dr Miguel Bernabeu, from a recent paper, was featured as the SINAPSE Image of the Month for February 2021.
![An original retinal image acquired using optical coherence tomography angiography (OCTA) technology, followed by the best segmentation approaches obtained applying three different vessel enhancement methods: optimally oriented flux (OOF), & two neural network architectures, U-Net & CS-Net.](https://www.ed.ac.uk/sites/default/files/styles/landscape_breakpoints_theme_uoe_mobile_1x/public/thumbnails/image/eiweb_02_feb_21._sinapse_image_of_the_month_20210203_edit.jpg?itok=wf0WqSbZ)
Courtesy of Ylenia Giarratano, Dr Tom MacGillivray & Dr Miguel Bernabeu, this image shows an original optical coherence tomography angiography (OCTA) image of the retina (left panel) acquired with the RTVue-XR Avanti OCT system, & the performance of three automated image segmentation methods used to perform vessel enhancement & binarization (right panels): an optimally oriented flux (OOF) handcrafted filter & two deep learning architectures, U-Net & CS-Net.
Evaluation metrics applied to automated segmentation results for retinal scan subimages from 11 individuals found the best performance was achieved by U-Net & CS-Net architectures, & identified OOF as the best handcrafted filter for applications where manually segmented data are not available to retrain those approaches.
The source code & the image dataset with associated ground truth manual segmentations have been made openly available to support standardization efforts in OCTA image segmentation.
The image is taken from a recent study published in Translational Vision Science & Technology:
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Edinburgh Imaging is delighted to see that an image courtesy of Ylenia Giarratano, Dr Tom MacGillivray & Dr Miguel Bernabeu, from a recent paper, was featured as the SINAPSE Image of the Month for February 2021. #OCTA
@giaylenia @mobernabeu @SINAPSECENTRE