Tissue clearing methods offer great promise to understand tissue organisation, but also present serious technical challenges. Generating high quality tissue labelling, developing tools for demonstrably reliable and accurate extraction, and eliminating baises through stereological technique, will establish a high standard for 3D quantitative data from cleared tissue. These challenges are met with StereoMate, an open-source image analysis framework for immunofluorescent labelling in cleared tissue. The platform facilitates the development of image segmentation protocols with rigorous validation, and extraction of object-level data in an automated and stereological manner. Mouse dorsal root ganglion neurones were assessed to validate this platform, which revealed a profound loss and shift in neurone size, and loss of axonal input and synaptic terminations within the spinal dorsal horn following their injury. In conclusion, the StereoMate platform provides a general-purpose automated stereological analysis platform to generate rich and unbiased object-level datasets from immunofluorescent data.