Structural Connectivity Enhanced Anisotropic 3D Network for Brain Midline Delineation

Abstract

Brain midline delineation can facilitate the clinical evaluation of brain midline shift, which has a pivotal role in the diagnosis and prognosis of various brain pathology. However, there are still challenges for brain midline delineation: 1) the largely deformed midline is hard to localize if mixed with severe cerebral hemorrhage; 2) the predicted midlines of recent methods are not smooth and continuous which violates the structural priority. To overcome these challenges, we propose an anisotropic three dimensional (3D) network with context-aware refinement (A3D-CAR) for brain midline modeling. The proposed network fuses 3D context from different two dimensional (2D) slices through asymmetric context fusion. To exploit the elongated structure of the midline, an anisotropic block is designed to balance the difference between the adjacent pixels in the horizontal and vertical directions. For maintaining the structural priority of a brain midline, we present a novel 3D connectivity regular loss (3D CRL) to penalize the disconnectivity between nearby coordinates. Extensive experiments on the CQ dataset and one in-house dataset show that the proposed method outperforms three state-of-the-art methods on four evaluation metrics without excessive computational burden.

Keywords

brain midline delineation / refinement network / structure prior / connectivity regular loss