Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging, and many retinal OCT analysis methods have been proposed. However, it is currently not clear how successful they are in interpreting the retinal fluid on OCT, which is due to the lack of standardized benchmarks. To address this, we organized a challenge RETOUCH in conjunction with MICCAI 2017, with eight teams participating. The challenge consisted of two tasks: fluid detection and fluid segmentation. It featured for the first time: all three retinal fluid types, with annotated images provided by two clinical centers, which were acquired with the three most common OCT device vendors from patients with two different retinal diseases. The analysis revealed that in the detection task, the performance on the automated fluid detection was within the inter-grader variability. However, in the segmentation task, fusing the automated methods produced segmentations that were superior to all individual methods, indicating the need for further improvements in the segmentation performance.

Additional Metadata
Keywords Evaluation, image classification, image segmentation, optical coherence tomography, retina
Persistent URL dx.doi.org/10.1109/TMI.2019.2901398, hdl.handle.net/1765/118626
Journal IEEE Transactions on Medical Imaging
Citation
Bogunovic, H. (Hrvoje), Venhuizen, F. (Freerk), Klimscha, S. (Sophie), Apostolopoulos, S. (Stefanos), Bab-Hadiashar, A, Bagci, U. (Ulas), … Schmidt-Erfurth, U. (Ursula). (2019). RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge. IEEE Transactions on Medical Imaging, 38(8), 1858–1874. doi:10.1109/TMI.2019.2901398