Abstract
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.
Abstract (translated)
椭圆形的先验信息在医学和自然图像特定任务中的图像分割准确性提升方面起着关键作用。现有的基于深度学习的分割方法，包括“Segment Anything Model（SAM）”，往往难以有效地生成具有椭圆形状的分割结果。本文提出了一种新方法，通过变分法将椭圆形的先验信息整合到基于深度学习的SAM图像分割技术中。所提出的方法建立了一个参数化的椭圆轮廓场，限制了分割结果与预定义的椭圆轮廓对齐。利用双算法，该模型可以无缝地融合图像特征和椭圆先验及空间正则化先验，从而大幅提高分割精度。通过将SAM分解为四个数学子问题，我们将变分椭圆先验整合到新的SAM网络结构设计中，确保了SAM的分割输出由椭圆形区域组成。在某些特定图像数据集上的实验结果表明，相较于原始的SAM方法，本研究的方法有所改进。
URL
https://arxiv.org/abs/2504.12556
