Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, No. 333 Nanchen Road, Baoshan District, Shanghai, 200444, People's Republic of China.
School of Mechatronic Engineering and Automation, Shanghai University, No. 333 Nanchen Road, Baoshan District, Shanghai, 200444, People's Republic of China.
Phys Med Biol. 2024 May 10;69(10). doi: 10.1088/1361-6560/ad3e5c.
Pancreas is one of the most challenging organs for Computed Tomograph (CT) image automatic segmentation due to its complex shapes and fuzzy edges. It is simple and universal to use the traditional segmentation method as a post-processor of deep learning method for segmentation accuracy improvement. As the most suitable traditional segmentation method for pancreatic segmentation, the active contour model (ACM), still suffers from the problems of weak boundary leakage and slow contour evolution speed. Therefore, a convenient post-processor for any deep learning methods using superpixel-based active contour model (SbACM) is proposed to improve the segmentation accuracy.Firstly, the superpixels with strong adhesion to edges are used to guide the design of narrowband and energy function. A multi-scale evolution strategy is also proposed to reduce the weak boundary leakage and comprehensively improve the evolution speed. Secondly, using the original image and the coarse segmentation results obtained from deep learning methods as inputs, the proposed SbACM method is used as a post-processor for fine segmentation. Finally, the pancreatic segmentation public dataset TCIA from the National Institutes of Health(NIH, USA) is used for evaluation, and the Wilcoxon Test confirmed that the improvement of proposed method is statistically significant.(1) the superpixel-based narrowband shape and dynamic edge energy of the proposed SbACM work for boundary leakage reduction, as well as the multi-scale evolution strategy and dynamic narrowband width for the evolution speed improvement; (2) as a post-processor, SbACM can increase the Dice similarity coefficients (DSC) of five typical UNet-based models, including UNet, SS-UNet, PBR UNet, ResDSN, and nnUNet, 2.35% in average and 9.04% in maximum. (3) Based on the best backbone nnUNet, the proposed post-processor performs better than either adding edge awareness or adding edge loss in segmentation enhancement without increasing the complexity and training time of deep learning models.The proposed SbACM can improve segmentation accuracy with the lowest cost, especially in cases of squeezed fuzzy edges with similar neighborhood , and complex edges.
胰腺是计算机断层扫描 (CT) 图像自动分割中最具挑战性的器官之一，因为它的形状复杂，边缘模糊。使用传统的分割方法作为深度学习方法的后处理器来提高分割准确性是简单而通用的。作为最适合胰腺分割的传统分割方法，主动轮廓模型 (ACM) 仍然存在边界泄漏弱和轮廓演化速度慢的问题。因此，提出了一种基于超像素的主动轮廓模型 (SbACM) 的便捷后处理器，以提高分割准确性。
首先，使用与边缘强粘附的超像素来指导窄带和能量函数的设计。还提出了一种多尺度演化策略，以减少弱边界泄漏并全面提高演化速度。其次，使用原始图像和从深度学习方法获得的粗分割结果作为输入，将所提出的 SbACM 方法用作精细分割的后处理器。最后，使用美国国立卫生研究院 (NIH) 的公共胰腺分割数据集 TCIA 进行评估，Wilcoxon 检验证实了所提出方法的改进在统计上是显著的。
(1) 所提出的 SbACM 的基于超像素的窄带形状和动态边缘能量有助于减少边界泄漏，以及多尺度演化策略和动态窄带宽度有助于提高演化速度；
(2) 作为后处理器，SbACM 可以将五个典型的基于 UNet 的模型（包括 UNet、SS-UNet、PBR UNet、ResDSN 和 nnUNet）的 Dice 相似系数 (DSC) 提高 2.35%，最高可达 9.04%。
(3) 在最佳骨干 nnUNet 的基础上，所提出的后处理器在不增加深度学习模型的复杂性和训练时间的情况下，在分割增强方面的表现优于添加边缘感知或添加边缘损失。
所提出的 SbACM 可以以最低的成本提高分割准确性，特别是在具有相似邻域和复杂边缘的挤压模糊边缘的情况下。
