  Anatomical consistency in biomarker segmentation is crucial for many medical
image analysis tasks. A promising paradigm for achieving anatomically
consistent segmentation via deep networks is incorporating pixel connectivity,
a basic concept in digital topology, to model inter-pixel relationships.
However, previous works on connectivity modeling have ignored the rich
channel-wise directional information in the latent space. In this work, we
demonstrate that effective disentanglement of directional sub-space from the
shared latent space can significantly enhance the feature representation in the
connectivity-based network. To this end, we propose a directional connectivity
modeling scheme for segmentation that decouples, tracks, and utilizes the
directional information across the network. Experiments on various public
medical image segmentation benchmarks show the effectiveness of our model as
compared to the state-of-the-art methods. Code is available at
https://github.com/Zyun-Y/DconnNet.
