ECONet：用于基于涂鸦的交互式分割的高效卷积在线似然网络
ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation
AI总结：
提出ECONet，一种在线学习的卷积神经网络，用于基于涂鸦的交互式分割，通过补丁训练和加权交叉熵损失，在COVID-19肺部病变标注中Dice分数提高16%，执行时间减少3倍。
摘要：
在CT图像中自动分割与COVID-19相关的肺部病变需要大量标注体积。标注需要专家知识，且通过完全手动分割方法获取耗时。此外，肺部病变在患者间存在较大差异，某些病理与健康肺组织在视觉上相似。这给应用现有的半自动交互式分割技术进行数据标注带来了挑战。为解决这些挑战，我们提出了一种高效的卷积神经网络（CNNs），该网络可在标注者提供基于涂鸦的交互时在线学习。为了仅从用户交互标注的样本中加速学习，我们采用基于补丁的方法训练网络。此外，我们使用加权交叉熵损失来解决用户交互可能导致的类别不平衡。在在线推理期间，使用全卷积方法将学习到的网络应用于整个输入体积。我们使用合成涂鸦将我们提出的方法与最先进的方法进行比较，并表明在标注与COVID-19相关的肺部病变任务上，它优于现有方法，Dice分数提高16%，执行时间减少3倍，且所需基于涂鸦的标注体素减少9000个。由于在线学习特性，我们的方法能快速适应用户输入，产生高质量的分割标签。ECONet的源代码可在以下网址获取：https://github.com/masadcv/ECONet-MONAILabel。
英文摘要：
Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes. Annotations mandate expert knowledge and are time-intensive to obtain through fully manual segmentation methods. Additionally, lung lesions have large inter-patient variations, with some pathologies having similar visual appearance as healthy lung tissues. This poses a challenge when applying existing semi-automatic interactive segmentation techniques for data labelling. To address these challenges, we propose an efficient convolutional neural networks (CNNs) that can be learned online while the annotator provides scribble-based interaction. To accelerate learning from only the samples labelled through user-interactions, a patch-based approach is used for training the network. Moreover, we use weighted cross-entropy loss to address the class imbalance that may result from user-interactions. During online inference, the learned network is applied to the whole input volume using a fully convolutional approach. We compare our proposed method with state-of-the-art using synthetic scribbles and show that it outperforms existing methods on the task of annotating lung lesions associated with COVID-19, achieving 16% higher Dice score while reducing execution time by 3$\times$ and requiring 9000 lesser scribbles-based labelled voxels. Due to the online learning aspect, our approach adapts quickly to user input, resulting in high quality segmentation labels. Source code for ECONet is available at: https://github.com/masadcv/ECONet-MONAILabel.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
