4th International Conference on Medical Imaging with Deep Learning Jul 07, 2021 - Jul 09, 2021 — Virtual Meeting (online)
Medical Image Understanding and Analysis Conference 2021 Jul 12, 2021 - Jul 14, 2021 — Virtual Meeting (online)
Medical Imaging Convention [rescheduled] Sep 15, 2021 - Sep 16, 2021 — National Exhibition Centre, Birmingham, England
2021 SINAPSE ASM Sep 16, 2021 - Sep 17, 2021 — Technology & Innovation Centre, University of Strathclyde, 99 George Street, Glasgow
Total Body PET 2021 conference [rescheduled] Sep 22, 2021 - Sep 24, 2021 — Virtual Meeting (online)


SINAPSE experts from around Scotland have developed ten online modules designed to explain medical imaging. They are freely available and are intended for non-specialists.

Edinburgh Imaging Academy at the University of Edinburgh offers the following online programmes through a virtual learning environment:

Neuroimaging for Research MSc/Dip/Cert

Imaging MSc/Dip/Cert

PET-MR Principles & Applications Cert

Applied Medical Image Analysis Cert

Online Short Courses

SINAPSE Image of the Month: OCTA retinal image segmentation

February 2021 SINAPSE Image of the Month


Courtesy of Ylenia Giarratano, Dr Tom MacGillivray and 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, and the performance of three automated image segmentation methods used to perform vessel enhancement and binarization (right panels): an optimally oriented flux (OOF) handcrafted filter and two deep learning architectures, U-Net and 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 and CS-Net architectures, and identified OOF as the best handcrafted filter for applications where manually segmented data are not available to retrain those approaches. The source code and 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:

Giarratano Y, Bianchi E, Gray C, Morris A, MacGillivray T, Dhillon B, Bernabeu MO. Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics. Transl Vis Sci Technol 2020; 9(13):5.