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.

翻译：椭圆形状先验信息在提高医学和自然图像特定任务分割精度中扮演关键角色。现有基于深度学习的分割方法（包括Segment Anything模型,SAM）往往难以高效生成具有椭圆形状的分割结果。本文提出一种新方法，利用变分法将椭圆形状先验融入基于深度学习的SAM图像分割技术。该方法建立参数化椭圆轮廓场，约束分割结果与预定义椭圆轮廓对齐。通过采用对偶算法，模型无缝融合图像特征与椭圆先验及空间正则化先验，从而显著提升分割精度。通过将SAM分解为四个数学子问题，我们集成变分椭圆先验设计出新的SAM网络结构，确保SAM的分割输出由椭圆区域构成。在特定图像数据集上的实验结果表明，该方法相比原始SAM具有更优性能。