Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway.
Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway.
Med Image Anal. 2023 Oct;89:102870. doi: 10.1016/j.media.2023.102870. Epub 2023 Jun 26.
A major barrier to applying deep segmentation models in the medical domain is their typical data-hungry nature, requiring experts to collect and label large amounts of data for training. As a reaction, prototypical few-shot segmentation (FSS) models have recently gained traction as data-efficient alternatives. Nevertheless, despite the recent progress of these models, they still have some essential shortcomings that must be addressed. In this work, we focus on three of these shortcomings: (i) the lack of uncertainty estimation, (ii) the lack of a guiding mechanism to help locate edges and encourage spatial consistency in the segmentation maps, and (iii) the models' inability to do one-step multi-class segmentation. Without modifying or requiring a specific backbone architecture, we propose a modified prototype extraction module that facilitates the computation of uncertainty maps in prototypical FSS models, and show that the resulting maps are useful indicators of the model uncertainty. To improve the segmentation around boundaries and to encourage spatial consistency, we propose a novel feature refinement module that leverages structural information in the input space to help guide the segmentation in the feature space. Furthermore, we demonstrate how uncertainty maps can be used to automatically guide this feature refinement. Finally, to avoid ambiguous voxel predictions that occur when images are segmented class-by-class, we propose a procedure to perform one-step multi-class FSS. The efficiency of our proposed methodology is evaluated on two representative datasets for abdominal organ segmentation (CHAOS dataset and BTCV dataset) and one dataset for cardiac segmentation (MS-CMRSeg dataset). The results show that our proposed methodology significantly (one-sided Wilcoxon signed rank test, p<0.05) improves the baseline, increasing the overall dice score with +5.2, +5.1, and +2.8 percentage points for the CHAOS dataset, the BTCV dataset, and the MS-CMRSeg dataset, respectively.
在医学领域应用深度分割模型的一个主要障碍是它们典型的数据饥渴性质，需要专家收集和标记大量数据进行训练。作为对此的反应，原型Few-Shot 分割（FSS）模型最近作为数据高效的替代方案引起了关注。然而，尽管这些模型最近取得了进展，但它们仍然存在一些必须解决的基本缺点。在这项工作中，我们专注于其中三个缺点：（i）缺乏不确定性估计，（ii）缺乏帮助定位边缘和鼓励分割图中空间一致性的引导机制，以及（iii）模型无法进行一步多类分割。我们没有修改或需要特定的骨干架构，提出了一种改进的原型提取模块，该模块有助于在原型 FSS 模型中计算不确定性图，并表明生成的图是模型不确定性的有用指标。为了改善边界周围的分割并鼓励空间一致性，我们提出了一种新颖的特征细化模块，该模块利用输入空间中的结构信息来帮助指导特征空间中的分割。此外，我们展示了如何使用不确定性图自动引导这种特征细化。最后，为了避免在逐类分割图像时出现模棱两可的体素预测，我们提出了一种执行一步多类 FSS 的过程。我们提出的方法的效率在两个用于腹部器官分割的代表性数据集（CHAOS 数据集和 BTCV 数据集）和一个用于心脏分割的数据集（MS-CMRSeg 数据集）上进行了评估。结果表明，我们提出的方法显著（单边 Wilcoxon 符号秩检验，p<0.05）提高了基线，分别将 CHAOS 数据集、BTCV 数据集和 MS-CMRSeg 数据集的总体骰子分数提高了+5.2、+5.1 和+2.8 个百分点。
