B-Scan Attentive CNN for the Classification of Retinal Optical Coherence Tomography Volumes

Optical coherence tomography (OCT) enables 3D cross-sectional imaging of the retinal tissues and has become an essential tool for the diagnosis of eye diseases. Clinically, the ophthalmologists examine each cross-sectional image (B- scan) of the 3D OCT volume to diagnose the retinal pathologies. How...

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Bibliographic Details
Published in:IEEE signal processing letters 2020, Vol.27, p.1025-1029
Main Authors: Das, Vineeta, Prabhakararao, Eedara, Dandapat, Samarendra, Bora, Prabin Kumar
Format: Article
Language:English
Subjects:
Artificial neural networks
Attention
Classification
convolutional neural network (CNN)
Cross-sections
Diagnosis
Diseases
Eye diseases
Feature extraction
Imaging
Medical personnel
Modules
Optical Coherence Tomography
optical coherence tomography (OCT) volume
Pathology
Reliability
Retina
Signs and symptoms
Three-dimensional displays
Tomography
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Summary:Optical coherence tomography (OCT) enables 3D cross-sectional imaging of the retinal tissues and has become an essential tool for the diagnosis of eye diseases. Clinically, the ophthalmologists examine each cross-sectional image (B- scan) of the 3D OCT volume to diagnose the retinal pathologies. However, this process is time-consuming and tedious. Automated methods in literature classify the individual B-scans and aggregate the diagnosis decision for the OCT volume using manual threshold-based rules. However, these methods lack generalizability and fail to incorporate the ophthalmologists' aspects of clinical diagnosis. Therefore, in this letter, we propose a B-scan attentive convolutional neural network (BACNN) that mimics the ophthalmologists' way of diagnosis by focusing on the clinically informative B- scans during the classification process. Specifically, a CNN based feature extraction module is used to extract spatial feature representations from the B-scans. Then, a self-attention module aggregates these features based on their clinical relevance to obtain a discriminative high-level feature vector for a reliable diagnosis. The experimental results on two clinical OCT datasets indicate that the proposed method provides improved classification performance. The method also demonstrates the correlation of the pathological symptoms in the B- scans with the attention weights.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2020.3000933