论文标题
3D医疗图像细分的斜线间环境残差学习
Inter-slice Context Residual Learning for 3D Medical Image Segmentation
论文作者
论文摘要
自动化和准确的3D医学图像分割在协助医学专业人员评估疾病进展并制定快速治疗时间表方面起着至关重要的作用。尽管深度卷积神经网络（DCNN）已广泛应用于此任务，但由于其对3D上下文感知的能力有限，这些模型的准确性仍然需要进一步改进。在本文中，我们提出了3D上下文残差网络（CONRESNET），以精确分割3D医学图像。该模型由编码器，分割解码器和上下文残差解码器组成。我们设计上下文残差模块，并使用它在每个刻度上桥接两个解码器。每个上下文残差模块都包含上下文残差映射和上下文注意映射，正式的旨在明确学习片间上下文信息，而后者则使用这种关注来提高细分精度。我们在MICCAI 2018脑瘤分段（BRAT）数据集和NIH胰腺分割（Pancreas-CT）数据集上评估了此模型。我们的结果不仅证明了拟议的3D上下文残差学习方案的有效性，而且还表明，所提出的conresnet比脑肿瘤分割中的六种顶级方法更准确，在胰腺细分中采用了七种顶级方法。代码可在https://git.io/conresnet上找到
Automated and accurate 3D medical image segmentation plays an essential role in assisting medical professionals to evaluate disease progresses and make fast therapeutic schedules. Although deep convolutional neural networks (DCNNs) have widely applied to this task, the accuracy of these models still need to be further improved mainly due to their limited ability to 3D context perception. In this paper, we propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images. This model consists of an encoder, a segmentation decoder, and a context residual decoder. We design the context residual module and use it to bridge both decoders at each scale. Each context residual module contains both context residual mapping and context attention mapping, the formal aims to explicitly learn the inter-slice context information and the latter uses such context as a kind of attention to boost the segmentation accuracy. We evaluated this model on the MICCAI 2018 Brain Tumor Segmentation (BraTS) dataset and NIH Pancreas Segmentation (Pancreas-CT) dataset. Our results not only demonstrate the effectiveness of the proposed 3D context residual learning scheme but also indicate that the proposed ConResNet is more accurate than six top-ranking methods in brain tumor segmentation and seven top-ranking methods in pancreas segmentation. Code is available at https://git.io/ConResNet
