论文标题
CT扫描的弱监督病变共裂部门
Weakly Supervised Lesion Co-segmentation on CT Scans
论文作者
论文摘要
医学成像中的病变细分是评估肿瘤大小和监测生长变化的有效工具。但是，手动病变细分不仅耗时，而且还昂贵，而且需要专家放射科医生的知识。因此，许多医院依靠一个宽松的替代品，称为实体瘤中的响应评估标准（Recist）。尽管这些注释远非精确，但它们在整个医院中被广泛使用，并且可以在其图片归档和通信系统（PAC）中找到。因此，这些注释有可能作为培训完整病变分割模型的较弱监督的强大而又具有挑战性的手段。在这项工作中，我们提出了一个弱监督的共裂系模型，该模型首先从recist切片中生成伪面具，并将其用作基于注意力的卷积神经网络，能够从一对CT扫描中分割常见病变。为了验证和测试该模型，我们使用了DeepLesion数据集，这是一个广泛的CT-SCAN病变数据集，包含32,735个PACS书签图像。广泛的实验结果证明了我们的共裂方法对病变分割的疗效，平均骰子系数为90.3％。
Lesion segmentation in medical imaging serves as an effective tool for assessing tumor sizes and monitoring changes in growth. However, not only is manual lesion segmentation time-consuming, but it is also expensive and requires expert radiologist knowledge. Therefore many hospitals rely on a loose substitute called response evaluation criteria in solid tumors (RECIST). Although these annotations are far from precise, they are widely used throughout hospitals and are found in their picture archiving and communication systems (PACS). Therefore, these annotations have the potential to serve as a robust yet challenging means of weak supervision for training full lesion segmentation models. In this work, we propose a weakly-supervised co-segmentation model that first generates pseudo-masks from the RECIST slices and uses these as training labels for an attention-based convolutional neural network capable of segmenting common lesions from a pair of CT scans. To validate and test the model, we utilize the DeepLesion dataset, an extensive CT-scan lesion dataset that contains 32,735 PACS bookmarked images. Extensive experimental results demonstrate the efficacy of our co-segmentation approach for lesion segmentation with a mean Dice coefficient of 90.3%.
