We address the 3D volume reconstruction problem from depth adjacent sub-volumes acquired by a confocal laser scanning microscope (CLSM). Our goal is to align the sub-volumes by estimating a set of optimal global transformations that preserve morphological continuity of medical structures, e.g., blood vessels, in the reconstructed 3D volume. We approach the problem by learning morphological characteristics of structures of interest in each sub-volume to understand global alignment transformations. Based on the observations of morphology, sub-volumes are aligned by connecting the morphological features at the sub-volume boundaries by minimizing morphological discontinuity. To minimize the discontinuity, we introduce three morphological discontinuity metrics: discontinuity magnitude at sub-volume boundary points, and overall and junction discontinuity residuals after polynomial curve fitting to multiple aligned sub-volumes. The proposed techniques have been applied to the problem of aligning CLSM sub-volumes acquired from four consecutive physical cross sections. Our experimental results demonstrated significant improvements of morphological smoothness of medical structures in comparison with the results obtained by feature matching at the sub-volume boundaries. The experimental results were evaluated by visual inspection and by quantifying morphological discontinuity metrics.

中文翻译：

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三维体积重建的轨迹融合。

我们解决了从共聚焦激光扫描显微镜 (CLSM) 获得的深度相邻子体积的 3D 体积重建问题。我们的目标是通过估计一组最佳全局变换来对齐子体积，这些变换在重建的 3D 体积中保持医疗结构（例如血管）的形态连续性。我们通过学习每个子卷中感兴趣结构的形态特征来理解全局对齐变换来解决这个问题。基于对形态的观察，通过最小化形态不连续性来连接子体积边界处的形态特征来对齐子体积。为了最小化不连续性，我们引入了三个形态不连续性度量：子体积边界点处的不连续性幅度，多项式曲线拟合到多个对齐的子体积后的整体和连接不连续性残差。所提出的技术已应用于对齐从四个连续物理横截面获取的 CLSM 子体积的问题。我们的实验结果表明，与通过子体积边界处的特征匹配获得的结果相比，医疗结构的形态平滑度得到了显着改善。通过目视检查和量化形态不连续性指标来评估实验结果。我们的实验结果表明，与通过子体积边界处的特征匹配获得的结果相比，医疗结构的形态平滑度得到了显着改善。通过目视检查和量化形态不连续性指标来评估实验结果。我们的实验结果表明，与通过子体积边界处的特征匹配获得的结果相比，医疗结构的形态平滑度得到了显着改善。通过目视检查和量化形态不连续性指标来评估实验结果。