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Introduction: Three-dimensional (3D) multicellular spheroids are fundamental in vitro tools for studying in vivo tissues. Volume is the main feature used for evaluating the drug/treatment effects, but several other features can be estimated even from a simple 2D image. For high-content screening analysis, the bottleneck is the segmentation stage, which is essential for detecting the spheroids in the images and then proceeding to the feature extraction stage for performing morphotypic analysis. Problem: Today, several tools are available for extracting morphological features from spheroid images, but all of them have pros and cons and there is no general validated solution. Thanks to new deep learning models, it is possible to standardize the process and adapt the analysis to big data. Novelty: Starting from the first version of AnaSP, an open-source software suitable for estimating several morphological features of 3D spheroids, we implemented a new module for automatically segmenting 2D brightfield images of spheroids by exploiting convolutional neural networks. Results: Several deep learning segmentation models (i.e., VVG16, VGG19, ResNet18, ResNet50) have been trained and compared. All of them obtained very interesting results and ResNet18 ranked as the best-performing. Conclusions: A network based on an 18-layer deep residual architecture (ResNet-18) has been integrated into AnaSP, releasing AnaSP 2.0, a version of the tool optimized for high-content screening analysis. The source code, standalone versions, user manual, sample images, video tutorial, and further documentation are freely available at: https://sourceforge.net/p/anasp. [ABSTRACT FROM AUTHOR]