Capsules for Object Segmentation
Capsules for Object Segmentation
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发表时间：
2018-04
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通讯作者：
Rodney LaLonde;Ulas Bagci
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作者：
Rodney LaLonde;Ulas Bagci
卷积神经网络（CNN）在过去几年中在广泛的计算机视觉任务中取得了显着的成果。Sabour等人最近引入的一种新架构，被称为具有动态路由的胶囊网络，对于数字识别和小图像分类已经显示出很好的初步结果。胶囊网络的成功在于它们能够通过用卷积步长和动态路由替换最大池化层来保留更多关于输入的信息，从而允许保留数据中的部分-整体关系。通过从输出胶囊向量重构输入来证明输入的这种保留。我们的工作在文献中首次将胶囊网络的使用扩展到对象分割任务。我们将卷积胶囊的思想扩展到局部连接路由，并提出了去卷积胶囊的概念。此外，我们扩展了掩码重建，以重建正输入类。所提出的卷积-去卷积胶囊网络（称为SegCaps）在参数空间大幅减少的情况下显示出对象分割任务的强大结果。作为一个示例应用，我们将所提出的SegCaps应用于从低剂量CT扫描中分割病理肺，并将其准确性和效率与其他基于U-Net的架构进行了比较。SegCaps能够处理大图像尺寸（512 x 512），而不是基线胶囊（通常小于32 x 32）。所提出的SegCaps将U-Net架构的参数数量减少了95.4%，同时仍然提供了更好的分割精度。
Convolutional neural networks (CNNs) have shown remarkable results over the last several years for a wide range of computer vision tasks. A new architecture recently introduced by Sabour et al., referred to as a capsule networks with dynamic routing, has shown great initial results for digit recognition and small image classification. The success of capsule networks lies in their ability to preserve more information about the input by replacing max-pooling layers with convolutional strides and dynamic routing, allowing for preservation of part-whole relationships in the data. This preservation of the input is demonstrated by reconstructing the input from the output capsule vectors. Our work expands the use of capsule networks to the task of object segmentation for the first time in the literature. We extend the idea of convolutional capsules with locally-connected routing and propose the concept of deconvolutional capsules. Further, we extend the masked reconstruction to reconstruct the positive input class. The proposed convolutional-deconvolutional capsule network, called SegCaps, shows strong results for the task of object segmentation with substantial decrease in parameter space. As an example application, we applied the proposed SegCaps to segment pathological lungs from low dose CT scans and compared its accuracy and efficiency with other U-Net-based architectures. SegCaps is able to handle large image sizes (512 x 512) as opposed to baseline capsules (typically less than 32 x 32). The proposed SegCaps reduced the number of parameters of U-Net architecture by 95.4% while still providing a better segmentation accuracy.