论文标题
自动分割脊柱多发性硬化病变：如何在MRI对比度上概括？
Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?
论文作者
论文摘要
尽管医疗图像细分最近有所改善，但跨成像对比度概括的能力仍然是一个空旷的问题。为了应对这一挑战，我们实施了特征线性调制（膜），以利用细分模型中的物理知识，并了解每个对比的特征。有趣的是，良好的U-NET在多对比度数据集（骰子得分的0.72）上达到了与我们的拍摄 - Unet相同的性能，这表明脊柱MS病变细分中有一个瓶颈，不同于不同对比的概括。这种瓶颈可能源于评估者间的变异性，在我们的数据集中估计骰子得分的0.61。
Despite recent improvements in medical image segmentation, the ability to generalize across imaging contrasts remains an open issue. To tackle this challenge, we implement Feature-wise Linear Modulation (FiLM) to leverage physics knowledge within the segmentation model and learn the characteristics of each contrast. Interestingly, a well-optimised U-Net reached the same performance as our FiLMed-Unet on a multi-contrast dataset (0.72 of Dice score), which suggests that there is a bottleneck in spinal MS lesion segmentation different from the generalization across varying contrasts. This bottleneck likely stems from inter-rater variability, which is estimated at 0.61 of Dice score in our dataset.
