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.

提出了一种 3D 医学图像分割和检测的容积注意力（VA）模块。VA 注意力受到视频处理的最新进展的启发，并使得 2.5D 网络能够利用沿着 z 方向的上下文信息，从而在训练数据有限时允许使用预先训练的 2D 检测模型。其与 Mask R-CNN 的整合显示，在肝肿瘤分割挑战中实现了最优表现，超越了以前的挑战赢家 3.9 分，并在 LiTS 排行榜上达到了最佳成绩。在 DeepLesion 数据集上的检测实验也表明，将 VA 添加到现有的物体检测器中，可以获得 69.1 的灵敏度，每张图像的 0.5 假阳性，超越了已发表的最佳结果 6.6 分。