Multi-color confocal scanning-laser microscopy (CSLM) allows examination of the relationships between neuronal somata and the nerve fibers surrounding them at sub-micron resolution in x,y, and z. Given these properties, it should be possible to use multi-color CSLM to identify relationships that might be synapses and eliminate those that are clearly too distant to be synapses. In previous studies of this type, pairs of images (e.g., red and green images for tissue stained with rhodamine and fluorescein) have been merged and examined for nerve terminals that appose a stained cell (see, for instance, Mason et al.). The above method suffers from two disadvantages, though. First, although it is possible to recognize appositions in which the varicosity abuts the cell in the x or y axes, it is more difficult to recognize them if the apposition is oriented at all in the z-axis—e.g., if the varicosity lies above or below the neuron rather than next to it. Second, using this method to identify potential appositions over an entire cell is time-consuming and tedious.
Artificial intelligence-assisted analysis of endoscopic retrograde cholangiopancreatography image for identifying ampulla and difficulty of selective cannulation
