Caenorhabditis elegans as an in vivo model organism provides the potential for higher throughput substance testing, leading to reduced animal testing, substance use, and experiment costs. In this work, white light and fluorescence images of closeup captures of C. elegans worms were used as a modality of measurement for the protein expression. To measure worm morphology and the effect of substances on the nematode’s behavior, fitness, and survivability relevant features will be extracted automatically. With automated segmentation and localization of worms in both modalities, important features can be extracted allowing conclusions on substance effects. For the segmentation, we used a Mask R-CNN to extract single worm instances and to allow the separation of close instances. Different effects on the training process and the combination of both image modalities were investigated. This results in a low MAPE and a high \(R^{2}\) on unseen C. elegans images for important morphological and protein expression features such as mean intensity ( \(R^{2}\)  = 0.995), length ( \(R^{2}\)  = 0.952) and area ( \(R^{2}\)  = 0.983).