Details

Autor(en) / Beteiligte

• Bogunovic, Hrvoje
• Venhuizen, Freerk
• Klimscha, Sophie
• Apostolopoulos, Stefanos
• Bab-Hadiashar, Alireza
• Bagci, Ulas
• Beg, Mirza Faisal
• Bekalo, Loza
• Chen, Qiang
• Ciller, Carlos
• Gopinath, Karthik
• Gostar, Amirali K.
• Jeon, Kiwan
• Ji, Zexuan
• Kang, Sung Ho
• Koozekanani, Dara D.
• Lu, Donghuan
• Morley, Dustin
• Parhi, Keshab K.
• Park, Hyoung Suk
• Rashno, Abdolreza
• Sarunic, Marinko
• Shaikh, Saad
• Sivaswamy, Jayanthi
• Tennakoon, Ruwan
• Yadav, Shivin
• De Zanet, Sandro
• Waldstein, Sebastian M.
• Gerendas, Bianca S.
• Klaver, Caroline
• Sanchez, Clara I.
• Schmidt-Erfurth, Ursula

Titel

RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge

Ist Teil von

• IEEE transactions on medical imaging, 2019-08, Vol.38 (8), p.1858-1874

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2019

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.