  Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging
technique that provides high-resolution cross-sectional images of the retina,
which are useful for diagnosing and monitoring various retinal diseases.
However, manual segmentation of OCTA images is a time-consuming and
labor-intensive task, which motivates the development of automated segmentation
methods. In this paper, we propose a novel multi-task learning method for OCTA
segmentation, called OCTA-MTL, that leverages an image-to-DT (Distance
Transform) branch and an adaptive loss combination strategy. The image-to-DT
branch predicts the distance from each vessel voxel to the vessel surface,
which can provide useful shape prior and boundary information for the
segmentation task. The adaptive loss combination strategy dynamically adjusts
the loss weights according to the inverse of the average loss values of each
task, to balance the learning process and avoid the dominance of one task over
the other. We evaluate our method on the ROSE-2 dataset its superiority in
terms of segmentation performance against two baseline methods: a single-task
segmentation method and a multi-task segmentation method with a fixed loss
combination.
