Abstract
This work aims to automatically detect cement lines in decalcified cortical bone sections stained with H&E. Employed is a methodology developed previously by the authors and proven to successfully count and disambiguate the micro-architectural features (namely Haversian canals, canaliculi, and osteocyte lacunae) present in the secondary osteons/Haversian system (osteon) of cortical bone. This methodology combines methods typically considered separately, namely pulse coupled neural networks (PCNN), particle swarm optimization (PSO), and adaptive threshold (AT). In lieu of human bone, slides (at 20× magnification) from bovid cortical bone are used in this study as proxy of human bone. Having been characterized, features with same orientation are used to detect the cement line viewed as the next coaxial layer adjacent to the outermost lamella of the osteon. Employed for this purpose are three attributes for each and every micro-sized feature identified in the osteon lamellar system: (1) orientation, (2) size (ellipse perimeter) and (3) Euler number (a topological measure). From a training image, automated parameters for the PCNN network are obtained by forming fitness functions extracted from these attributes. It is found that a 3-way combination of these features attributes yields good representations of the overall osteon boundary (cement line). Near-unity values of classical metrics of quality (precision, sensitivity, specificity, accuracy, and dice) suggest that the segments obtained automatically by the optimized artificial intelligent methodology are of high fidelity as compared with manual tracing. For bench marking, cement lines segmented by k-means did not fare as well. An analysis based on the modified Hausdorff distance (MHD) of the segmented cement lines also testified to the quality of the detected cement lines vis-a-vis the k-means method.
Highlights
Medical microscopy continues to produce increasingly higher resolution images and presenting opportunities to observe ever more detailed microscopic pathologies
The authors utilized an AI-based methodology that combined pulse coupled neural networks (PCNN), particle swarm optimization (PSO), and adaptive threshold (AT) and where PSO optimization fitness functions were constructed based on entropy and energy [22] or on the micro-sized features’ geometric attributes such as size and shape [23]
A methodology developed by Hage and Hamade [22,23] is based on combining pulse coupled neural networks PCNN [26,27], particle swarm optimization PSO [28], and adaptive threshold AT [29,30] methods
Summary
Medical microscopy continues to produce increasingly higher resolution images and presenting opportunities to observe ever more detailed microscopic pathologies. Micro-features (lacunae, canaliculi, Haversian canals) were segmented via color thresholding of bone images using the k-means clustering method. The authors utilized an AI-based methodology that combined PCNN (pulse coupled neural networks), PSO (particle swarm optimization), and AT (adaptive threshold) and where PSO optimization fitness functions were constructed based on entropy and energy [22] or on the micro-sized features’ geometric attributes such as size and shape [23]. Both approaches yielded high fidelity feature segmentation of said salient micro features. The modified Hausdorff distance (MHD) method was used to verify the quality of the segmented cement lines using our methodology vis-a-vis those detected by the k-means method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
