OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions
OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions
复制标题
DOI：
10.1016/j.media.2019.02.006
复制
发表时间：
2019-05-01
影响因子：
10.9
通讯作者：
Bouteldja, Nassim
中科院分区：
文献类型：
--
作者：
Heinrich, Mattias P.;Oktay, Ozan;Bouteldja, Nassim
深度网络通过在端到端可训练的体系结构中用学习的卷积过滤器取代手工制作的功能，在大多数图像分析任务中设置了最先进的技术。尽管如此，卷积网络的规格仍然受到许多人工设计的影响--卷积运算的接收野的形状和大小是一个非常敏感的部分，必须针对不同的图像分析应用进行调整。跳跃连接的3D全卷积多尺度结构擅长语义分割和标志点定位，具有巨大的内存需求和对大量注释数据集的依赖，这是医学图像分析广泛适用的一个重要限制。基于空间变换网络的可微图像内插原理，提出了一种新颖而有效的基于可训练3D卷积核的方法，该方法在连续空间学习滤波系数和空间滤波偏移量。在两个具有挑战性的3D CT多器官分割任务中，与完全卷积U网结构相比，结合了这种二进制极大且具有拐点的稀疏核(方尖碑)过滤器的深层网络需要更少的可训练参数和更少的内存，同时获得高质量的结果。大量的验证实验表明，稀疏可变形卷积的性能是因为它们能够用很少有表现力的过滤器参数来捕捉大的空间背景，并且网络深度并不总是学习复杂的形状和外观特征所必需的。与传统CNN的结合进一步改善了对形状变化较大的小器官的描绘，并且使用灵活的图像采样的快速推断时间可能为深层网络在计算机辅助、图像引导的干预中提供新的潜在用途。(C)《2019年》，爱思唯尔出版。
Deep networks have set the state-of-the-art in most image analysis tasks by replacing handcrafted features with learned convolution filters within end-to-end trainable architectures. Still, the specifications of a convolutional network are subject to much manual design - the shape and size of the receptive field for convolutional operations is a very sensitive part that has to be tuned for different image analysis applications. 3D fully-convolutional multi-scale architectures with skip-connection that excel at semantic segmentation and landmark localisation have huge memory requirements and rely on large annotated datasets - an important limitation for wider adaptation in medical image analysis.We propose a novel and effective method based on trainable 3D convolution kernels that learns both filter coefficients and spatial filter offsets in a continuous space based on the principle of differentiable image interpolation first introduced for spatial transformer network. A deep network that incorporates this one binary extremely large and inflecting sparse kernel (OBELISK) filter requires fewer trainable parameters and less memory while achieving high quality results compared to fully-convolutional U-Net architectures on two challenging 3D CT multi-organ segmentation tasks.Extensive validation experiments indicate that the performance of sparse deformable convolutions is due to their ability to capture large spatial context with few expressive filter parameters and that network depth is not always necessary to learn complex shape and appearance features. A combination with conventional CNNs further improves the delineation of small organs with large shape variations and the fast inference time using flexible image sampling may offer new potential use cases for deep networks in computer-assisted, image-guided interventions. (C) 2019 Published by Elsevier B.V.