Tracking-assisted Detection of Dendritic Spines in Time-Lapse Microscopic Images

Publication date: Available online 19 October 2018Source: NeuroscienceAuthor(s): Lavdie Rada, Bike Kilic, Ertunc Erdil, Yazmín Ramiro-Cortés, Inbal Israely, Devrim Unay, Mujdat Cetin, Ali Özgür ArgunsahAbstractDetecting morphological changes of dendritic spines in time-lapse microscopy images and correlating them with functional properties such as memory and learning, are fundamental and challenging problems in neurobiology research. In this paper, we propose an algorithm for dendritic spine detection in time series. The proposed approach initially performs spine detection at each time point and improves the accuracy by exploiting the information obtained from tracking of individual spines over time. To detect dendritic spines in a time point image we employ an SVM classifier trained by pre-labeled SIFT feature descriptors in combination with a dot enhancement filter. Second, to track the growth or loss of spines, we apply a SIFT-based rigid registration method for the alignment of time-series images. This step takes into account both the structure and the movement of objects, combined with a robust dynamic scheme to link information about spines that disappear and reappear over time. Next, we improve spine detection by employing a probabilistic dynamic programming approach to search for an optimum solution to accurately detect missed spines. Finally, we determine the spine location more precisely by performing a watershed-geodesic active contour model. We quantitatively ...

Source: Neuroscience - November 2, 2018 Category: Neuroscience Source Type: research