  The segmentation of atrial scan images is of great significance for the
three-dimensional reconstruction of the atrium and the surgical positioning.
Most of the existing segmentation networks adopt a 2D structure and only take
original images as input, ignoring the context information of 3D images and the
role of prior information. In this paper, we propose an atrium segmentation
network LGSANet with location guidance and siamese adjustment, which takes
adjacent three slices of images as input and adopts an end-to-end approach to
achieve coarse-to-fine atrial segmentation. The location guidance(LG) block
uses the prior information of the localization map to guide the encoding
features of the fine segmentation stage, and the siamese adjustment(SA) block
uses the context information to adjust the segmentation edges. On the atrium
datasets of ACDC and ASC, sufficient experiments prove that our method can
adapt to many classic 2D segmentation networks, so that it can obtain
significant performance improvements.
