Enhancing Vessel Continuity in Deep Learning based Segmentation using Maximum Intensity Projection as Loss

Vessel Segmentation with deep learning is a challenging task that involves not only learning high-level feature representations but also the spatial continuity of the features across dimensions. Semi-supervised patch-based approaches have been effective in identifying small vessels of 1-2 voxels in diameter but failed to maintain vessel continuity. This study focuses on improving the segmentation quality by considering the spatial correlation of the features using the maximum intensity projection (MIP) as an additional loss criterion. It was observed that the proposed method quantitatively improves the segmentation while also improving vessel continuity, as evident in the visual examinations of ROIs.