Hematoma Expansion Context Guided Intracranial Hemorrhage Segmentation and Uncertainty Estimation.

Accurate segmentation of the Intracranial Hemorrhage (ICH) in non-contrast CT images is significant for computer-aided diagnosis. Although existing methods have achieved remarkable results, none of them ever incorporated ICH's prior information in their methods. In this work, for the first time, we proposed a novel SLice EXpansion Network (SLEX-Net), which incorporated hematoma expansion in the segmentation architecture by directly modeling the spatial variation of hematoma expansion. Firstly, a new module named Slice Expansion Module (SEM) was built, which can effectively transfer contextual information between two adjacent slices by mapping predictions from one slice to another. Secondly, to perceive label correlation information from both upper and lower slices, we designed two information transmission paths: forward and backward slice expansion. By further exploiting intra-slice and inter-slice context with the information paths, the network significantly improved the accuracy and continuity of segmentation results. Moreover, the proposed SLEX-Net enables us to conduct an uncertainty estimation with one-time inference, which is much more efficient than existing methods. We evaluated the proposed SLEX-Net and compared it with some state-of-the-art methods. Experimental results demonstrate that our method makes significant improvements in all metrics on segmentation performance and outperforms other existing uncertainty estimation methods in terms of several metrics. The code will be available from https://github.com/JohnleeHIT/SLEX-Net.