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1.	Suveer, Amit, et al. (författare) Automated detection of cilia in low magnification transmission electron microscopy images using template matching 2016 Ingår i: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on. - : IEEE. - 9781479923496 - 9781479923502 ; , s. 386-390 Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract Ultrastructural analysis using Transmission Electron Microscopy (TEM) is a common approach for diagnosing primary ciliary dyskinesia. The manually performed diagnostic procedure is time consuming and subjective, and automation of the process is highly desirable. We aim at automating the search for plausible cilia instances in images at low magnification, followed by acquisition of high magnification images of regions with detected cilia for further analysis. This paper presents a template matching based method for automated detection of cilia objects in low magnification TEM images, where object radii do not exceed 10 pixels. We evaluate the performance of a series of synthetic templates generated for this purpose by comparing automated detection with results manually created by an expert pathologist. The best template achieves a detection at equal error rate of 47% which suffices to identify densely populated cilia regions suitable for high magnification imaging.

Suveer, Amit, et al. (författare)
Automated detection of cilia in low magnification transmission electron microscopy images using template matching
2016
Ingår i: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on. - : IEEE. - 9781479923496 - 9781479923502 ; , s. 386-390
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Ultrastructural analysis using Transmission Electron Microscopy (TEM) is a common approach for diagnosing primary ciliary dyskinesia. The manually performed diagnostic procedure is time consuming and subjective, and automation of the process is highly desirable. We aim at automating the search for plausible cilia instances in images at low magnification, followed by acquisition of high magnification images of regions with detected cilia for further analysis. This paper presents a template matching based method for automated detection of cilia objects in low magnification TEM images, where object radii do not exceed 10 pixels. We evaluate the performance of a series of synthetic templates generated for this purpose by comparing automated detection with results manually created by an expert pathologist. The best template achieves a detection at equal error rate of 47% which suffices to identify densely populated cilia regions suitable for high magnification imaging.

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