论文标题
3D医疗图像分割和检测的体积注意
Volumetric Attention for 3D Medical Image Segmentation and Detection
论文作者
论文摘要
提出了用于3D医疗图像分割和检测的体积注意（VA）模块。 VA的注意力灵感来自视频处理的最新进展，使2.5D网络能够沿z方向利用上下文信息，并在培训数据受到限制时允许使用预验证的2D检测模型，就像医疗应用程序一样。显示其在面膜R-CNN中的集成能够在肝脏肿瘤分段（LITS）挑战上实现最先进的表现，在纸张提交时表现优于先前的挑战率3.9分，并在LITS排行榜上取得了最佳表现。深层数据集上的检测实验还表明，在现有对象检测器中添加VA可以使69.1的灵敏度为0.5假阳性图像，表现优于最佳发布结果6.6分。
A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction, and allows the use of pretrained 2D detection models when training data is limited, as is often the case for medical applications. Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge winner by 3.9 points and achieving top performance on the LiTS leader board at the time of paper submission. Detection experiments on the DeepLesion dataset also show that the addition of VA to existing object detectors enables a 69.1 sensitivity at 0.5 false positive per image, outperforming the best published results by 6.6 points.
