论文标题
分割一致的概率病变计数
Segmentation-Consistent Probabilistic Lesion Counting
论文作者
论文摘要
病变计数是疾病严重程度，患者预后和治疗效果的重要指标，但是将其视为医学成像中的任务通常被忽略为有利于分割。这项工作引入了一种新型的连续区分函数，该功能以一致的方式将病变分割预测映射到病变计数概率分布。所提出的端到端方法包括体素聚类，病变水平的体素概率聚集和泊松二比计数 - 是非参数，因此提供了一种具有强大而一致的方法，可增强病变段具有HOC后计数能力。关于gadolinium增强病变计数的实验表明，我们的方法输出了准确且精心校准的计数分布，以捕获有意义的不确定性信息。他们还揭示了我们的模型适合于病变细分的多任务学习，在低数据制度中是有效的，并且对对抗性攻击是可靠的。
Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical imaging is often overlooked in favor of segmentation. This work introduces a novel continuously differentiable function that maps lesion segmentation predictions to lesion count probability distributions in a consistent manner. The proposed end-to-end approach--which consists of voxel clustering, lesion-level voxel probability aggregation, and Poisson-binomial counting--is non-parametric and thus offers a robust and consistent way to augment lesion segmentation models with post hoc counting capabilities. Experiments on Gadolinium-enhancing lesion counting demonstrate that our method outputs accurate and well-calibrated count distributions that capture meaningful uncertainty information. They also reveal that our model is suitable for multi-task learning of lesion segmentation, is efficient in low data regimes, and is robust to adversarial attacks.
