IEEE Trans Med Imaging. 2018 Oct;37(10):2236-2247. doi: 10.1109/TMI.2018.2840047. Epub 2018 May 23.
Tracing tubular structures from biomedical images is important for a wide range of applications. Particularly, the spermatozoon is an essential cell whose flagella have a tubular form. Its main function is to fertilize the egg, and the flagellum is fundamental to achieve this task which depends importantly on the dynamics of intracellular calcium ([Ca]). Measuring [Ca] along the flagellum in 3-D is not a simple matter since it requires: 1) sophisticated fluorescence imaging techniques dealing with low intensity and signal to noise ratio (SNR) and 2) tracing the flagellum's centerline. Most of the algorithms proposed to trace tubular structures have been developed for multi-branch structures not being adequate for single tubular structures with low SNR. Taking into account the prior knowledge that the flagellum is constituted by a single tubular structure, we propose an automatic method to trace and track multiple single tubular structures from 3-D images. First, an algorithm based on one-class classification allows enhancement of the flagellum. This enhanced 3-D image permits guiding an iterative centerline algorithm toward the flagellum's centerline. Each sperm is assigned an ID to keep track of it in 3-D . Our algorithm was quantitatively evaluated using a ground truth 564 semi-manual traces (six 3-D image stacks) comparing them to those obtained from state-of-the-art tubular structure centerline extraction algorithms. The qualitative and quantitative results show that our algorithm is extracting similar traces as compared with ground truth, and it is more robust and accurate to trace the flagellum's centerline than multi-branch algorithms.
从生物医学图像中追踪管状结构对于广泛的应用非常重要。特别是,精子是一种重要的细胞,其鞭毛具有管状形式。它的主要功能是使卵子受精,鞭毛对于实现这一任务至关重要,这在很大程度上取决于细胞内钙 ([Ca]) 的动力学。由于需要:1)处理低强度和信噪比 (SNR) 的复杂荧光成像技术,以及 2)追踪鞭毛的中心线,因此在 3-D 中测量鞭毛中的 [Ca] 并非易事。大多数用于追踪管状结构的算法都是针对多分支结构开发的,对于 SNR 较低的单个管状结构并不适用。考虑到鞭毛由单个管状结构组成的先验知识,我们提出了一种从 3-D 图像中自动追踪和跟踪多个单个管状结构的方法。首先,基于一类分类的算法允许增强鞭毛。此增强的 3-D 图像允许引导迭代中心线算法朝向鞭毛的中心线。为了在 3-D 中跟踪每个精子,我们为其分配一个 ID。我们的算法使用 564 个半自动跟踪的真实数据(六个 3-D 图像堆栈)进行了定量评估,将其与最先进的管状结构中心线提取算法获得的跟踪结果进行了比较。定性和定量结果表明,与真实数据相比,我们的算法提取的跟踪结果相似,并且与多分支算法相比,它更稳健、更准确地追踪鞭毛的中心线。