DISCOVER: 2-D Multiview Summarization of Optical Coherence Tomography
  Angiography for Automatic Diabetic Retinopathy Diagnosis
Mostafa El Habib Daho Yihao Li Rachid Zeghlache Hugo Le Boité Pierre Deman Laurent Borderie Hugang Ren Niranchana Mannivanan Capucine Lepicard Béatrice Cochener Aude Couturier Ramin Tadayoni Pierre-Henri Conze Mathieu Lamard Gwenolé Quellec
Diabetic Retinopathy (DR), an ocular complication of diabetes, is a leading
cause of blindness worldwide. Traditionally, DR is monitored using Color Fundus
Photography (CFP), a widespread 2-D imaging modality. However, DR
classifications based on CFP have poor predictive power, resulting in
suboptimal DR management. Optical Coherence Tomography Angiography (OCTA) is a
recent 3-D imaging modality offering enhanced structural and functional
information (blood flow) with a wider field of view. This paper investigates
automatic DR severity assessment using 3-D OCTA. A straightforward solution to
this task is a 3-D neural network classifier. However, 3-D architectures have
numerous parameters and typically require many training samples. A lighter
solution consists in using 2-D neural network classifiers processing 2-D
en-face (or frontal) projections and/or 2-D cross-sectional slices. Such an
approach mimics the way ophthalmologists analyze OCTA acquisitions: 1) en-face
flow maps are often used to detect avascular zones and neovascularization, and
2) cross-sectional slices are commonly analyzed to detect macular edemas, for
instance. However, arbitrary data reduction or selection might result in
information loss. Two complementary strategies are thus proposed to optimally
summarize OCTA volumes with 2-D images: 1) a parametric en-face projection
optimized through deep learning and 2) a cross-sectional slice selection
process controlled through gradient-based attribution. The full summarization
and DR classification pipeline is trained from end to end. The automatic 2-D
summary can be displayed in a viewer or printed in a report to support the
decision. We show that the proposed 2-D summarization and classification
pipeline outperforms direct 3-D classification with the advantage of improved
interpretability.