Shan Guoping, Bai Xue, Ge Yun, Wang Binbing
School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China.
Department of Radiation Physics, Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
PLoS One. 2025 Jan 30;20(1):e0317801. doi: 10.1371/journal.pone.0317801. eCollection 2025.
Accurate and efficient automatic segmentation is essential for various clinical tasks such as radiotherapy treatment planning. However, atlas-based segmentation still faces challenges due to the lack of representative atlas dataset and the computational limitations of deformation algorithms. In this work, we have proposed an atlas selection procedure (subset atlas grouping approach, MAS-SAGA) which utilized both image similarity and volume features for selecting the best-fitting atlases for contour propagation. A dataset of anonymized female pelvic Computed Tomography (CT) images demonstrated that MAS-SAGA significantly outperforms conventional multi-atlas-based segmentation (cMAS) in terms of Dice Similarity Coefficient (DSC) and 95th Percentile Hausdorff Distance (95HD) for bladder and rectum segmentation using a three-fold cross-validation strategy. The proposed procedure also reduced computation time compared to cMAS, making it a promising tool for medical image analysis applications. In addition, we have evaluated two distinct atlas selection methods: the Feature-based Atlas Selection Approach (MAS-FASA) and the Similarity-based Atlas Selection Approach (MAS-SIM). We investigate the differences between these two methods in terms of their ability to select the best fitting atlases. The findings demonstrated that MAS-FASA selected different atlases than MAS-SIM, resulting in improved segmentation performance overall. It highlighted the potential of feature-based subgrouping techniques in enhancing the efficacy of MAS algorithms in the field of medical image segmentation.
准确而高效的自动分割对于诸如放射治疗计划等各种临床任务至关重要。然而，基于图谱的分割由于缺乏具有代表性的图谱数据集以及变形算法的计算局限性，仍然面临挑战。在这项工作中，我们提出了一种图谱选择程序（子集图谱分组方法，MAS-SAGA），该程序利用图像相似度和体积特征来选择最适合轮廓传播的图谱。一个匿名女性盆腔计算机断层扫描（CT）图像数据集表明，在使用三倍交叉验证策略进行膀胱和直肠分割时，MAS-SAGA在骰子相似系数（DSC）和第95百分位数豪斯多夫距离（95HD）方面显著优于传统的基于多图谱的分割（cMAS）。与cMAS相比，所提出的程序还减少了计算时间，使其成为医学图像分析应用中有前景的工具。此外，我们评估了两种不同的图谱选择方法：基于特征的图谱选择方法（MAS-FASA）和基于相似度的图谱选择方法（MAS-SIM）。我们研究了这两种方法在选择最适合图谱的能力方面的差异。研究结果表明，MAS-FASA选择的图谱与MAS-SIM不同，总体上提高了分割性能。它突出了基于特征的分组技术在提高医学图像分割领域中MAS算法功效方面的潜力。
