Accurate segmentation of the Intracranial Hemorrhage (ICH) in non-contrast CT images is significant for computer-aided diagnosis. Although existing methods have achieved remarkable 1 1The code will be available from https://github.com/JohnleeHIT/SLEX-Net.results, none of them 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 hematoma variation among adjacent slices. 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 contextual information from both upper and lower slices, we designed two information transmission paths: forward and backward slice expansion, and aggregated results from those paths with a novel weighing strategy. 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.

中文翻译：

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血肿扩张上下文引导颅内出血分割和不确定性估计


非增强 CT 图像中颅内出血 (ICH) 的准确分割对于计算机辅助诊断具有重要意义。尽管现有方法已经取得了显着的成果 1 1代码可从 https://github.com/JohnleeHIT/SLEX-Net.results 获得，但它们都没有在其方法中纳入 ICH 的先验信息。在这项工作中，我们首次提出了一种新颖的 SLice 扩展网络（SLEX-Net），它通过直接建模相邻切片之间的血肿变化，将血肿扩展纳入分割架构中。首先，构建了一个名为切片扩展模块（SEM）的新模块，它可以通过将预测从一个切片映射到另一个切片来有效地在两个相邻切片之间传输上下文信息。其次，为了感知上下切片的上下文信息，我们设计了两种信息传输路径：前向和后向切片扩展，并通过新颖的权重策略聚合这些路径的结果。通过进一步利用信息路径的切片内和切片间上下文，网络显着提高了分割结果的准确性和连续性。此外，所提出的 SLEX-Net 使我们能够通过一次性推理进行不确定性估计，这比现有方法要高效得多。我们评估了所提出的 SLEX-Net 并将其与一些最先进的方法进行了比较。实验结果表明，我们的方法在分割性能的所有指标上都取得了显着的改进，并且在多个指标方面优于其他现有的不确定性估计方法。