论文标题
自适应对比度学习具有多相器官分割的动态相关性
Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation
论文作者
论文摘要
最近的研究表明，将``扫描方面的''对比标签引入多相计算机断层扫描（CT）的多器官学习中的卓越性能。但是，这种扫描的标签受到限制：（1）粗糙的分类，无法捕获跨所有跨所有Organs跨所有Organs的细粒度的``细节'''fine-grom groun classification; （2）通常手动提供标签（即对比阶段），这是容易出错的，并且可能引入定义阶段的手动偏见。在本文中，我们提出了一种新型的数据驱动的对比损失函数，该函数适应器官级的每个Minibatch中样品之间的相似/不同对比度。具体而言，由于器官之间存在变化级别的对比度，我们假设器官级别的对比差异可以带来其他上下文来定义潜在空间中的表示。在单热注意图下，通过平均器官强度计算器官对比度相关矩阵。适应器官驱动的相关矩阵的目的是建模在不同阶段的特征可分离性水平。我们使用非对比度CT（NCCT）数据集和MICCAI 2015 BTCV挑战对比度Enhance CT（CECT）数据集评估了我们提出的关于多器官分割的方法。 Compared to the state-of-the-art approaches, our proposed contrastive loss yields a substantial and significant improvement of 1.41% (from 0.923 to 0.936, p-value$<$0.01) and 2.02% (from 0.891 to 0.910, p-value$<$0.01) on mean Dice scores across all organs with respect to NCCT and CECT cohorts.我们通过MICCAI 2021 Flare Challenge Cect数据集进一步评估了训练有素的模型性能，并实现了平均掷骰子得分从0.927提高到0.934（p值$ <$ <0.01）。该代码可在以下网址找到：https：//github.com/masilab/dcc_cl
Recent studies have demonstrated the superior performance of introducing ``scan-wise" contrast labels into contrastive learning for multi-organ segmentation on multi-phase computed tomography (CT). However, such scan-wise labels are limited: (1) a coarse classification, which could not capture the fine-grained ``organ-wise" contrast variations across all organs; (2) the label (i.e., contrast phase) is typically manually provided, which is error-prone and may introduce manual biases of defining phases. In this paper, we propose a novel data-driven contrastive loss function that adapts the similar/dissimilar contrast relationship between samples in each minibatch at organ-level. Specifically, as variable levels of contrast exist between organs, we hypothesis that the contrast differences in the organ-level can bring additional context for defining representations in the latent space. An organ-wise contrast correlation matrix is computed with mean organ intensities under one-hot attention maps. The goal of adapting the organ-driven correlation matrix is to model variable levels of feature separability at different phases. We evaluate our proposed approach on multi-organ segmentation with both non-contrast CT (NCCT) datasets and the MICCAI 2015 BTCV Challenge contrast-enhance CT (CECT) datasets. Compared to the state-of-the-art approaches, our proposed contrastive loss yields a substantial and significant improvement of 1.41% (from 0.923 to 0.936, p-value$<$0.01) and 2.02% (from 0.891 to 0.910, p-value$<$0.01) on mean Dice scores across all organs with respect to NCCT and CECT cohorts. We further assess the trained model performance with the MICCAI 2021 FLARE Challenge CECT datasets and achieve a substantial improvement of mean Dice score from 0.927 to 0.934 (p-value$<$0.01). The code is available at: https://github.com/MASILab/DCC_CL
