Tracking-assisted Detection of Dendritic Spines in Time-Lapse Microscopic Images.
Authors of this article are:
Rada L, Kilic B, Erdil E, Ramiro-Cortés Y, Israely I, Unay D, Cetin M, Argunsah AÖ.
A summary of the article is shown below:
Detecting 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 assess the performance of the proposed spine detection algorithm based on annotations performed by biologists and compare its performance with the results obtained by the noncommercial software NeuronIQ. Experiments show that our approach can accurately detect and quantify spines in 2-photon microscopy time-lapse data and is able to accurately identify spine elimination and formation.
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This article is a good source of information and a good way to become familiar with topics such as:
ICP;ROI;SIFT;SVM;TTX;curve evolution;dendritic spine detection;image processing;iterative closest point;learning spine dynamics;region of interest;scale invariant feature transform;support vector machine;tetrodotoxin;time-lapse images;tracking
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