论文标题
通过多尺度移位型神经网络的体积地标检测
Volumetric landmark detection with a multi-scale shift equivariant neural network
论文作者
论文摘要
深度神经网络在包括里程碑式检测在内的各种计算机视觉应用中产生了令人鼓舞的结果。对于临床CT扫描等体积图像中准确的解剖学地标检测的主要挑战是，大规模数据通常会限制由于GPU存储器限制而导致的神经网络结构的能力，这又可能限制了输出的精度。我们提出了一种多尺度的端到端深度学习方法，该方法在3D图像中实现了快速和记忆有效的地标检测。我们的架构由移动等级网络组成，每个网络都以不同的空间规模执行具有里程碑意义的检测。这些块从粗糙的尺度连接，具有可区分的重采样层，因此可以一起训练所有级别。我们还提出了一种噪声注入策略，可提高模型的鲁棒性，并使我们能够在测试时量化不确定性。我们评估了在263 CT体积上检测颈动脉分叉的方法，并以2.81mm的平均欧几里得距离误差获得比最先进的精度更好。
Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection. A major challenge for accurate anatomical landmark detection in volumetric images such as clinical CT scans is that large-scale data often constrain the capacity of the employed neural network architecture due to GPU memory limitations, which in turn can limit the precision of the output. We propose a multi-scale, end-to-end deep learning method that achieves fast and memory-efficient landmark detection in 3D images. Our architecture consists of blocks of shift-equivariant networks, each of which performs landmark detection at a different spatial scale. These blocks are connected from coarse to fine-scale, with differentiable resampling layers, so that all levels can be trained together. We also present a noise injection strategy that increases the robustness of the model and allows us to quantify uncertainty at test time. We evaluate our method for carotid artery bifurcations detection on 263 CT volumes and achieve a better than state-of-the-art accuracy with mean Euclidean distance error of 2.81mm.
