<i>CCM-Net: C</i> ontrastive and <i>C</i> onsistent <i>M</i> ulti- <i>T</i> ask Network for Artifact Segmentation and Quality Classification of OCTA Images

Artifacts are prevalent in Optical Coherence Tomography Angiography (OCTA) images, which probably interfere doctor’s diagnosis and greatly limit its utility. Therefore, it is desirable to segment artifacts and assess quality when using them for diagnosis. In this article, we propose an end-to-end network (named CCM-Net: C ontrastive and C onsistent M ulti-task Network) to jointly address artifact segmentation and quality classification of OCTA images. We first devise multiple Task-Specific Attention Blocks to integrate deep features at different CNN layers for segmenting artifacts and classifying the quality of the input OCTA image. In this way, the weights of different deep features can be automatically learned and are not the same for the two tasks. Moreover, we devise a contrastive loss and a consistency loss to leverage sample relations for further enhancing prediction accuracy. Specifically, given an input OCTA image, we first augment it with a color jitter and select another OCTA image with the same quality classification label. We then design a contrastive loss so that the segmentation results of the input OCTA image are similar to its enhanced OCTA image, while the segmentation results of the two selected OCTA images are not similar. Besides, we devise a consistency loss on the classification results of the three images, because we can find that these images have the same quality classification labels. Experiments on an in-house OCTA dataset (Multi-OCTA) demonstrate that the proposed CCM-Net outperforms state-of-the-art methods.