In life sciences, there are increasing interest in 3D culture models to better reproduce the 3D environment encountered in-vivo. Imaging of such 3D culture models is instrumental for drug discovery, but face several issues before its use becomes widespread. Extensive microscopic investigation of these 3D cell models faces the challenge of light penetration in depth in opaque biological tissues. To overcome this limit, diverse clearing techniques have emerged over the past decades. However, it is not straightforward to choose the best clearing protocols, and assess quantitatively their clearing efficiency. Focusing on spheroids, we propose a combination of fast and cost-effective clearing procedure for such medium-sized samples. A generic method with local contrast metrics and deep convolutional neural network-based segmentation of nuclei is proposed to quantify the efficiency of clearing. We challenged this method by testing the possibility to transfer segmentation knowledge from a clearing protocol to another. The later results support the pertinence of training deep learning algorithms on cleared samples to further use the segmentation pipeline on non-cleared ones. This second step of the protocol gives access to digital clearing possibilities applicable to live and high-throughput optical imaging.