用椭圆轮廓先验提升SAM在医学与自然图像中的分割精度
Contour Field based Elliptical Shape Prior for the Segment Anything Model
- 构建参数化椭圆轮廓场，约束分割结果为椭圆形状
- 在特定数据集上显著优于原始SAM，提升分割准确性
- 适合需要精确椭圆结构分割的医学影像分析场景
椭圆形状先验在医学和自然图像的特定任务中对提升图像分割精度至关重要。现有基于深度学习的分割方法，包括通用分割模型SAM，通常难以高效生成符合椭圆形状的分割结果。本文提出一种新方法，通过变分法将椭圆形状先验融入SAM框架。该方法建立参数化的椭圆轮廓场，约束分割结果与预设椭圆轮廓对齐。利用对偶算法，模型可无缝融合图像特征、椭圆先验及空间正则化先验，显著提升分割精度。通过将SAM分解为四个数学子问题，引入变分椭圆先验，设计出新型网络结构，确保输出结果包含椭圆区域。在多个特定图像数据集上的实验表明，该方法相比原始SAM有明显改进。
原文摘要 · Abstract (English)
The elliptical shape prior information plays a vital role in improving the accuracy of image segmentation for specific tasks in medical and natural images. Existing deep learning-based segmentation methods, including the Segment Anything Model (SAM), often struggle to produce segmentation results with elliptical shapes efficiently. This paper proposes a new approach to integrate the prior of elliptical shapes into the deep learning-based SAM image segmentation techniques using variational methods. The proposed method establishes a parameterized elliptical contour field, which constrains the segmentation results to align with predefined elliptical contours. Utilizing the dual algorithm, the model seamlessly integrates image features with elliptical priors and spatial regularization priors, thereby greatly enhancing segmentation accuracy. By decomposing SAM into four mathematical sub-problems, we integrate the variational ellipse prior to design a new SAM network structure, ensuring that the segmentation output of SAM consists of elliptical regions. Experimental results on some specific image datasets demonstrate an improvement over the original SAM.
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