BACKGROUND: To investigate the intricate nervous processes involved in many biological activities by computerized image analysis, accurate and reproducible labeling and measurement of neurites are prerequisite. We have developed an automated neurite analysis method to assist this task. METHODS: Our approach can be considered as automated with certain user interaction in setting initial parameters. Single and connected centerlines along neurites are extracted. The computerized method can also generate branching and end points. Owing to its multi-scale flexibility, both thick and thin neurites are simultaneously detected. RESULTS: We employ the relative neurite length difference (defined as the difference between the lengths obtained by automated and manual analysis divided by the total length of the latter) and neurite centerline deviation (defined as the area of the regions enclosed by different paths between automated and manual analysis divided by the total length of the former) to evaluate the performance of our algorithm, which is of great interest in neurite analysis. The average of the relative length difference is about 0.02, while the average of the centerline deviation is about 2.8 pixels. The probabilities of the distributions being the same from the Kolmogorov-Smirnov (KS) test of the automatic and manual results are 99.79%. The KS test also shows no significant bias between different observers based on the proposed new validation scheme. CONCLUSIONS: With the accurate and automated extraction of neurite centerlines and measurement of neurite lengths, the proposed method, which greatly reduces human labor and improves efficiency, can serve as a candidate tool for large-scale neurite analysis beyond the capability of manual tracing methods. Copyright 2006 International Society for Analytical Cytology.
BACKGROUND: To investigate the intricate nervous processes involved in many biological activities by computerized image analysis, accurate and reproducible labeling and measurement of neurites are prerequisite. We have developed an automated neurite analysis method to assist this task. METHODS: Our approach can be considered as automated with certain user interaction in setting initial parameters. Single and connected centerlines along neurites are extracted. The computerized method can also generate branching and end points. Owing to its multi-scale flexibility, both thick and thin neurites are simultaneously detected. RESULTS: We employ the relative neurite length difference (defined as the difference between the lengths obtained by automated and manual analysis divided by the total length of the latter) and neurite centerline deviation (defined as the area of the regions enclosed by different paths between automated and manual analysis divided by the total length of the former) to evaluate the performance of our algorithm, which is of great interest in neurite analysis. The average of the relative length difference is about 0.02, while the average of the centerline deviation is about 2.8 pixels. The probabilities of the distributions being the same from the Kolmogorov-Smirnov (KS) test of the automatic and manual results are 99.79%. The KS test also shows no significant bias between different observers based on the proposed new validation scheme. CONCLUSIONS: With the accurate and automated extraction of neurite centerlines and measurement of neurite lengths, the proposed method, which greatly reduces humanlabor and improves efficiency, can serve as a candidate tool for large-scale neurite analysis beyond the capability of manual tracing methods. Copyright 2006 International Society for Analytical Cytology.
Authors: Ryan A Kerekes; Rodrigo A P Martins; Denise Davis; Mahmut Karakaya; Shaun Gleason; Michael A Dyer Journal: Neurochem Res Date: 2011-01-08 Impact factor: 3.996