Capillary detection in transverse muscle sections

Ahmad Nadim Baharum; Moi Hoon Yap; Glenn Ferris; Ezak Ahmad Fadzrin; Hans Degens

doi:10.1117/12.2292998

12 March 2018 Capillary detection in transverse muscle sections

Ahmad Nadim Baharum, Moi Hoon Yap, Glenn Ferris, Ezak Ahmad Fadzrin, Hans Degens

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105781X (2018) https://doi.org/10.1117/12.2292998
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Manual identification of capillaries in transverse muscle sections is laborious and time consuming. Although the process of classifying a structure as a capillary is facilitated by (immuno)histochemical staining methods, human judgement is still required in a significant number of cases. This is mainly due to the fact that not all capillaries stain as strongly: they may have an elongated appearance and/or there may be staining artefacts that would lead to a false identification of a capillary. Here we propose two automated methods of capillary detection: a novel image processing approach and an existing machine learning approach that has been previously used to detect nuclei-shaped objects. The robustness of the proposed methods was tested on two sets of differently stained muscle sections. On average, the image processing approach scored a True Positive Rate of 0.817 and a harmonic mean (F1 measure) of 0.804 whilst the machine learning approach scored a True Positive Rate 0.843 and F1 measure of 0.846. Both proposed methods are thus able to mimic most of the manual capillary detection, but further improvements are required for practical applications.

Citation Download Citation

Ahmad Nadim Baharum, Moi Hoon Yap, Glenn Ferris, Ezak Ahmad Fadzrin, and Hans Degens "Capillary detection in transverse muscle sections", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105781X (12 March 2018); https://doi.org/10.1117/12.2292998

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