Self-Supervised Bulk Motion Artifact Removal in Optical Coherence Tomography Angiography
This work addresses image quality issues in bioengineering tasks like OCTA imaging, offering a more efficient and effective solution for artifact removal, though it appears incremental by building on existing learning-based inpainting approaches.
The paper tackles the problem of bulk motion artifacts degrading optical coherence tomography angiography images by proposing a self-supervised content-aware removal model that extracts structural and appearance features from artifact areas, enabling it to remove larger artifacts with better visualization results, as demonstrated on mouse cortex data where it outperforms previous methods.
Optical coherence tomography angiography (OCTA) is an important imaging modality in many bioengineering tasks. The image quality of OCTA, however, is often degraded by Bulk Motion Artifacts (BMA), which are due to micromotion of subjects and typically appear as bright stripes surrounded by blurred areas. State-of-the-art methods usually treat BMA removal as a learning-based image inpainting problem, but require numerous training samples with nontrivial annotation. In addition, these methods discard the rich structural and appearance information carried in the BMA stripe region. To address these issues, in this paper we propose a self-supervised content-aware BMA removal model. First, the gradient-based structural information and appearance feature are extracted from the BMA area and injected into the model to capture more connectivity. Second, with easily collected defective masks, the model is trained in a self-supervised manner, in which only the clear areas are used for training while the BMA areas for inference. With the structural information and appearance feature from noisy image as references, our model can remove larger BMA and produce better visualizing result. In addition, only 2D images with defective masks are involved, hence improving the efficiency of our method. Experiments on OCTA of mouse cortex demonstrate that our model can remove most BMA with extremely large sizes and inconsistent intensities while previous methods fail.