arXiv 2310.080842023-10-12cs.CV

基于涂鸦标注和形状先验的体医学图像分割

Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors

Shanghai Jiao Tong University（上海交通大学）；Ruijin Hospital, Shanghai Jiao Tong University of Medicine（上海交通大学医学院附属瑞金医院）；Shanghai Jiao Tong University of Medicine（上海交通大学医学院）

Qiuhui Chen, Haiying Lyu, Xinyue Hu, Yong Lu, Yi Hong

备注：: arXiv admin note: text overlap with arXiv:2205.06779

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AI总结：

本文提出Scribble2D5，一种基于涂鸦标注和形状先验的弱监督体医学图像分割方法，通过标签传播和边界预测提升3D各向异性图像分割的边界准确性。

摘要：

近年来，使用涂鸦等弱标注的弱监督图像分割在计算机视觉和医学图像分析领域引起了广泛关注，因为与耗时且劳动密集的像素/体素级标注相比，此类标注更容易获取。然而，由于缺乏对感兴趣区域（ROIs）的结构监督，现有的基于涂鸦的方法在边界定位方面表现不佳。此外，大多数现有方法是为2D图像分割设计的，如果直接应用于每个图像切片，则无法充分利用体信息。在本文中，我们提出了一种基于涂鸦的体图像分割方法Scribble2D5，该方法处理3D各向异性图像分割并旨在改善其边界预测。为实现这一目标，我们通过提出的标签传播模块增强了一个2.5D注意力UNet，以扩展涂鸦的语义信息，并使用静态和主动边界预测的组合来学习ROI的边界并规范其形状。此外，我们提出了一个可选的附加组件，该组件利用来自未配对分割掩码的形状先验信息来进一步提高模型准确性。在三个公共数据集和一个私有数据集上的大量实验表明，我们的Scribble2D5在使用涂鸦和形状先验（如果可用）的体图像分割中达到了最先进的性能。

英文摘要：

Recently, weakly-supervised image segmentation using weak annotations like scribbles has gained great attention in computer vision and medical image analysis, since such annotations are much easier to obtain compared to time-consuming and labor-intensive labeling at the pixel/voxel level. However, due to a lack of structure supervision on regions of interest (ROIs), existing scribble-based methods suffer from poor boundary localization. Furthermore, most current methods are designed for 2D image segmentation, which do not fully leverage the volumetric information if directly applied to each image slice. In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and aims to its improve boundary prediction. To achieve this, we augment a 2.5D attention UNet with a proposed label propagation module to extend semantic information from scribbles and use a combination of static and active boundary prediction to learn ROI's boundary and regularize its shape. Also, we propose an optional add-on component, which incorporates the shape prior information from unpaired segmentation masks to further improve model accuracy. Extensive experiments on three public datasets and one private dataset demonstrate our Scribble2D5 achieves state-of-the-art performance on volumetric image segmentation using scribbles and shape prior if available.

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。