用于医学图像分割的尺寸感知跨形状涂鸦监督
Size Aware Cross-shape Scribble Supervision for Medical Image Segmentation
AI总结：
针对医学图像涂鸦监督中标注不一致及难适应多尺度目标的问题，提出跨形状标注、伪掩码及尺寸感知多分支三种方法，在多个息肉数据集上显著提升mDice并超越现有最先进方法。
摘要：
涂鸦监督是弱监督学习的一种常见形式，通过手绘曲线标注像素，有助于降低人工标注成本。该技术已广泛应用于医学图像分割任务以加速网络训练。然而，涂鸦监督在样本间标注一致性及全面真实信息获取方面存在局限。此外，它常面临难以适应不同尺度目标的挑战，这在医学图像中尤为突出。本文提出三种新方法以克服上述挑战：1）跨形状涂鸦标注方法；2）基于跨形状的伪掩码方法；3）尺寸感知多分支方法。研究深入探讨了参数与结构设计。实验结果表明，所提方法在多个息肉数据集上的mDice分数取得显著提升。值得注意的是，这些方法的组合表现优于专为医学图像分割设计的最先进涂鸦监督方法。
英文摘要：
Scribble supervision, a common form of weakly supervised learning, involves annotating pixels using hand-drawn curve lines, which helps reduce the cost of manual labelling. This technique has been widely used in medical image segmentation tasks to fasten network training. However, scribble supervision has limitations in terms of annotation consistency across samples and the availability of comprehensive groundtruth information. Additionally, it often grapples with the challenge of accommodating varying scale targets, particularly in the context of medical images. In this paper, we propose three novel methods to overcome these challenges, namely, 1) the cross-shape scribble annotation method; 2) the pseudo mask method based on cross shapes; and 3) the size-aware multi-branch method. The parameter and structure design are investigated in depth. Experimental results show that the proposed methods have achieved significant improvement in mDice scores across multiple polyp datasets. Notably, the combination of these methods outperforms the performance of state-of-the-art scribble supervision methods designed for medical image segmentation.
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