Increasing Sensitivity of Ca2+ Spark Detection in Noisy Images by Application of a Matched-Filter Object Detection Algorithm
Increasing Sensitivity of Ca2+ Spark Detection in Noisy Images by Application of a Matched-Filter Object Detection Algorithm
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DOI：
10.1529/biophysj.108.135251
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发表时间：
2008-12-15
影响因子：
3.4
通讯作者：
Cannell, Mark B.
中科院分区：
文献类型：
--
作者：
Kong, Cherrie H. T.;Soeller, Christian;Cannell, Mark B.
微观钙 (Ca2+) 事件（例如 Ca2+ 火花）是一个重要的研究领域，因为它们有助于阐明细胞内信号传导的机制。在心脏中，Ca2+ 火花是由于 Ca2+ 通过兰尼碱受体通道从肌内质网释放而发生的。 Ca2+火花特性的测量可以提供有关原位兰尼碱受体通道门控控制的有价值的信息，但需要高时空分辨率成像，这会产生对火花检测存在问题的噪声数据集。自动检测算法可以克服视觉检测偏差，但漏检和误报事件可能会扭曲测量的 Ca2+ 火花特性的分布。我们提出了一种灵敏可靠的方法，用于自动检测使用共焦线扫描或全内反射荧光显微镜获得的数据集中的 Ca2+ 火花。这种匹配过滤器检测算法 (MFDA) 采用用户定义的对象，选择模拟 Ca2+ 火花特性，并在实验数据集中搜索该对象的实例。检测的确定性由非参数统计测试提供。提供的代码也可以重新。根据检测到的对象重新定位搜索对象，进一步提高检测灵敏度。与常用的强度阈值算法相比，MFDA 更加灵敏和可靠，特别是在低信噪比的情况下。 MFDA 还可以轻松适应噪声数据集中的其他信号检测问题。
Microscopic calcium (Ca2+) events ( such as Ca2+ sparks) are an important area for study, as they help clarify the mechanism(s) underlying intracellular signaling. In the heart, Ca2+ sparks occur as a result of Ca2+ release from the sarcoendoplasmic reticulum, via ryanodine receptor channels. Measurement of Ca2+ spark properties can provide valuable information about the control of ryanodine receptor channel gating in situ, but requires high spatiotemporal resolution imaging, which produces noisy datasets that are problematic for spark detection. Automated detection algorithms may overcome visual detection bias, but missed and false-positive events can distort the distribution of measured Ca2+ spark properties. We present a sensitive and reliable method for the automated detection of Ca2+ sparks in datasets obtained using confocal line-scanning or total internal reflection fluorescence microscopy. This matched-filter detection algorithm(MFDA) employs a user-defined object, chosen to mimic Ca2+ spark properties, and the experimental dataset is searched for instances of the object. Detection certainty is provided by nonparametric statistical testing. The supplied codes can also re. ne the search object on the basis of those detected to further increase detection sensitivity. In comparison to a commonly used, intensity-thresholding algorithm, the MFDA is more sensitive and reliable, particularly at low signal/noise ratios. The MFDA can also be easily adapted to other signal-detection problems in noisy datasets.