Computer-aided diagnosis tools for Retinopathy of Prematurity (ROP) base their decisions on handcrafted retinal features that highly correlate with expert diagnoses, such as arterial and venous curvature, tortuosity and dilation. Deep learning leads to performance comparable to those of expert physicians, albeit not ensuring that the same clinical factors are learned in the deep representations. In this paper, we investigate the relationship between the handcrafted and the deep learning features in the context of ROP diagnosis. Average statistics on the handcrafted features for each input image were expressed as retinal concept measures. Three disease severity grades, i.e. normal, pre-plus and plus, were classified by a deep convolutional neural network. Regression Concept Vectors (RCV) were computed in the network feature space for each retinal concept measure. Relevant concept measures were identified by bidirectional relevance scores for the normal and plus classes. Results show that the curvature, diameter and tortuosity of the segmented vessels are indeed relevant to the classification. Among the potential applications of this method, the analysis of borderline cases between the classes and of network faults, in particular, can be used to improve the performance.
Retinopathy of prematurity (ROP) is a disease that affects premature infants, where abnormal growth of the retinal blood vessels can lead to blindness unless treated accordingly. Infants considered at risk of severe ROP are monitored for symptoms of plus disease, characterized by arterial tortuosity and venous dilation at the posterior pole, with a standard photographic definition. Disagreement among ROP experts in diagnosing plus disease has driven the development of computer-based methods that classify images based on hand-crafted features extracted from the vasculature. However, most of these approaches are semi-automated, which are time-consuming and subject to variability. In contrast, deep learning is a fully automated approach that has shown great promise in a wide variety of domains, including medical genetics, informatics and imaging. Convolutional neural networks (CNNs) are deep networks which learn rich representations of disease features that are highly robust to variations in acquisition and image quality. In this study, we utilized a U-Net architecture to perform vessel segmentation and then a GoogLeNet to perform disease classification. The classifier was trained on 3,000 retinal images and validated on an independent test set of patients with different observed progressions and treatments. We show that our fully automated algorithm can be used to monitor the progression of plus disease over multiple patient visits with results that are consistent with the experts’ consensus diagnosis. Future work will aim to further validate the method on larger cohorts of patients to assess its applicability within the clinic as a treatment monitoring tool.
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