Ordered iterative colour channel selection enhances segmentation of brain slice image neurons of the sham fetal sheep model

Abstract

• We propose a novel segmentation model to extract the densely populated overlapping cortical neurons from the background image, which is essential to rapidly qualify and quantify the brain cortical neurons from brain slice images of in utero fetal sheep model . • The ordered iterative colour channel selection (OICCS) segmentation model enhances the performance of the marker controlled watershed (MCW) K-means segmentation technique. • The OICCS model involves the iterative application of the MCW K-means segmentation method in the three colour channels (red, green, and blue) of an RGB image in a selective order. • The segmentation model is significant as it is the essential first step to accelerate the histological analysis process over the currently employed manual assessment. Hypoxic Ischemia (HI) accounts for 23% of annual worldwide neonatal death, brain damage and long-term disability. Histopathological studies of infants with HI are rare, so the assessment of neuronal survival in HI animal models compared to the neuronal survival of their sham control, such as the term in utero fetal sheep model, is pivotal to increasing our understanding of HI at the cellular level and for the development of novel therapeutics and treatments. Histopathological studies of the in utero fetal sheep model rely heavily on the manual identification of neurons in histological brain slice images. However, manual identification is prone to being highly subjective, costly and time-consuming. Thus, there is an urgent need to automate the segmentation process, which is complicated by the existence of densely populated overlapping cells with no prior information of shape or size, resulting in images of high complexity. In this article, we propose an ‘Ordered Iterative Colour Channel Selection (OICCS) method. Such that, when applied to the standard ‘Marker Controlled Watershed k-means segmentation’ (MCW k-means) method, serves to effectively segment cortical neurons from the background in brain slice images of the term equivalent sham control in utero fetal sheep model. Thus, tackling the over-segmentation issue that was encountered by MCW k-means alone. In the OICCS model, we determine that by iteratively applying MCW k-means segmentation: firstly, to the blue channel image; then again on the green channel image for cortical neurons that remained unsegmented from the blue channel segmentation; and finally again on the red channel image for cortical neurons that remained unsegmented from the green channel segmentation that optimal performance results could be achieved of 97.98% ± 0.97% over 90.65 ± 3.32% achieved by the standard MCW k-means applied to the R, G, B channel alone or other permutations of the colour channels. The new OICCS enhanced MCW k-means segmentation method presented here is a significant step in automating the segmentation process of cortical brain cells from brain slice images of the sham control fetal sheep model. This will serve to accelerate the histological segmentation process over the manually intensive identification process that is currently employed.