Registration between the baseline and follow-up lung computed tomography (CT) volumes plays an important role in computer-aided diagnosis and following-up care during adaptive radiotherapy. Diffeomorphic log-Demons as state of the art in Demons implementations is restricted to relatively small deformations, low accuracy, and ignoring some prior structural features. In this paper, an automated superpixel-borders-guided deformation image registration (SBG-DIR) algorithm is proposed. The proposed SBG-DIR method uses the simple linear iterative clustering (SLIC) algorithm to automated superpixels generation. Incorporation of superpixel borders into registration algorithm is implemented by a new similarity criterion based on the binary volume representation of superpixel borders. The binary volume representation enables accurate preserving motion boundaries, contributes to a faster convergence of the objective function and eliminates errors caused by manual interaction. In addition, a subtraction volume is produced by the intensity difference between the first time point CT volume and its warped follow-up CT volume. The subtraction volume can be used for detection of tumor tissue growth or shrinkage, which is an essential part of a CT-based diagnosis. Moreover, to ensure the topology preservation of biological objects, our proposed SBG-DIR method is implemented in the space of diffeomorphisms, in which meaningful biological shapes can be found. Compared with the state-of-the-art Demons, the proposed SBG-DIR method doesn’t require any additional optimization, yields a faster convergence and is more accurate and efficient in recovering large deformations. Experimental results indicate that the proposed SBG-DIR method performed better than the state-of-the-art Demons algorithms.

基线和后续肺计算机断层扫描 (CT) 体积之间的配准在自适应放射治疗期间的计算机辅助诊断和后续护理中起着重要作用。Diffeomorphic log-Demons 作为 Demons 实现中的最新技术，仅限于相对较小的变形、低精度，并且忽略了一些先前的结构特征。在本文中，提出了一种自动超像素边界引导的变形图像配准（SBG-DIR）算法。所提出的 SBG-DIR 方法使用简单的线性迭代聚类 (SLIC) 算法来自动生成超像素。将超像素边界纳入配准算法是通过一种基于超像素边界二进制体积表示的新相似性准则实现的。二进制体积表示能够准确地保留运动边界，有助于更快地收敛目标函数并消除由手动交互引起的错误。此外，第一时间点CT体积与其扭曲的后续CT体积之间的强度差异产生减法体积。减影体积可用于检测肿瘤组织的生长或收缩，这是基于 CT 的诊断的重要组成部分。此外，为了确保生物对象的拓扑保存，我们提出的 SBG-DIR 方法在微分同胚空间中实现，其中可以找到有意义的生物形状。与最先进的 Demons 相比，所提出的 SBG-DIR 方法不需要任何额外的优化，产生更快的收敛，并且在恢复大变形方面更准确和更有效。实验结果表明，所提出的 SBG-DIR 方法的性能优于最先进的 Demons 算法。