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.

本文提出了Scribble2D5，一种基于涂鸦的体积图像分割方法，旨在改善3D异向图像分割和边界预测。通过引入标签传播模块，扩展了涂鸦的语义信息，并使用静态和主动边界预测的组合学习感兴趣区域的边界并规范其形状，与此同时，通过融合未配对分割掩码的形状先验信息进一步提高模型的准确性。通过在三个公开数据集和一个私有数据集上的广泛实验，证明了Scribble2D5在使用涂鸦和形状先验数据进行体积图像分割方面取得了最先进的性能。

通过草图标注和形状先验进行体积医学图像分割