通过概率形状模型解析聚集蠕虫
RESOLVING CLUSTERED WORMS VIA PROBABILISTIC SHAPE MODELS.
作者信息
Wählby Carolina, Riklin-Raviv Tammy, Ljosa Vebjorn, Conery Annie L, Golland Polina, Ausubel Frederick M, Carpenter Anne E
机构信息
Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA.
出版信息
Proc IEEE Int Symp Biomed Imaging. 2010 Jun 21;2010(14-17 April 2010):552-555. doi: 10.1109/ISBI.2010.5490286.
Abstract
The roundworm Caenorhabditis elegans is an effective model system for biological processes such as immunity, behavior, and metabolism. Robotic sample preparation together with automated microscopy and image analysis has recently enabled high-throughput screening experiments using C. elegans. So far, such experiments have been limited to per-image measurements due to the tendency of the worms to cluster, which prevents extracting features from individual animals.We present a novel approach for the extraction of individual C. elegans from clusters of worms in high-throughput microscopy images. The key ideas are the construction of a low-dimensional shape-descriptor space and the definition of a probability measure on it. Promising segmentation results are shown.
摘要
蛔虫秀丽隐杆线虫是用于免疫、行为和新陈代谢等生物过程的有效模型系统。机器人样本制备与自动显微镜和图像分析相结合，最近使得使用秀丽隐杆线虫进行高通量筛选实验成为可能。到目前为止，由于线虫倾向于聚集，此类实验仅限于逐图像测量，这阻碍了从单个动物中提取特征。我们提出了一种从高通量显微镜图像中的线虫群中提取单个秀丽隐杆线虫的新方法。关键思想是构建一个低维形状描述符空间并在其上定义一个概率测度。展示了有前景的分割结果。
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