In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets. These issues may result in segmentations that lack the requisite precision for clinical analysis and can be misleadingly overconfident without associated uncertainty quantification. We propose the PULASki for biomedical image segmentation that accurately captures variability in expert annotations, even in small datasets. Our approach makes use of an improved loss function based on statistical distances in a conditional variational autoencoder structure (Probabilistic UNet), which improves learning of the conditional decoder compared to the standard cross-entropy particularly in class imbalanced problems. We analyse our method for two structurally different segmentation tasks (intracranial vessel and multiple sclerosis (MS) lesion) and compare our results to four well-established baselines in terms of quantitative metrics and qualitative output. Empirical results demonstrate the PULASKi method outperforms all baselines at the 5\% significance level. The generated segmentations are shown to be much more anatomically plausible than in the 2D case, particularly for the vessel task. Our method can also be applied to a wide range of multi-label segmentation tasks and and is useful for downstream tasks such as hemodynamic modelling (computational fluid dynamics and data assimilation), clinical decision making, and treatment planning.

翻译：在医学影像领域，许多基于监督学习的分割方法面临多重挑战，包括多位专家标注的高度变异性、标注数据稀缺以及类别不平衡数据集。这些问题可能导致分割结果缺乏临床分析所需的精度，且因缺乏不确定性量化而可能产生误导性的过度自信。我们提出PULASki用于生物医学图像分割，该方法即使在小数据集中也能准确捕获专家标注的变异性。我们的方法基于条件变分自编码器结构（Probabilistic UNet）中利用统计距离改进的损失函数，相比标准交叉熵，尤其在类别不平衡问题中能更有效地学习条件解码器。我们分别对两种结构不同的分割任务（颅内血管和多发性硬化（MS）病灶）进行分析，并从定量指标和定性输出两方面与四个成熟基线方法进行对比。实证结果表明，PULASki方法在5%显著性水平上优于所有基线。生成的分割结果在解剖学合理性上显著优于二维情况，尤其在血管任务中表现突出。该方法还可广泛应用于多种多标签分割任务，并适用于血流动力学建模（计算流体力学与数据同化）、临床决策及治疗规划等下游任务。