Directional latent space representation for medical image segmentation

Excellent medical image segmentation plays an important role in computer-aided diagnosis. Deep mining of pixel semantics is crucial for medical image segmentation. However, previous works on medical semantic segmentation usually overlook the importance of embedding subspace, and lacked the mining of latent space direction information. In this work, we construct global orthogonal bases and channel orthogonal bases in the latent space, which can significantly enhance the feature representation. We propose a novel distance-based segmentation method that decouples the embedding space into sub-embedding spaces of different classes, and then implements pixel level classification based on the distance between its embedding features and the origin of the subspace. Experiments on various public medical image segmentation benchmarks have shown that our model is superior compared to state-of-the-art methods. The code will be published at https://github.com/lxt0525/LSDENet.