For the first time in literature, we investigate the capability of Generative Adversarial Networks (GAN) for synthesizing realistic images of microsurgical procedures and augmenting training data for surgical tool detection. We employ videos from practice and intraoperative neurosurgical procedures to train and evaluate two recent GAN models that have shown promise in high-resolution image generation: StyleGAN2 with Adaptive Discriminator Augmentation and StyleGAN2 with Differential Augmentation. Models were trained with limited data for both conditional and unconditional image generation, where the conditional models generated images with and without surgical tools. Our results show that the unconditional models achieved FID scores between 6 and 25 units lower than the conditional models for the two practice datasets. The best performance (FID= 42.16 and 25.17) was achieved in the Go-around practice task and was comparable to the previous benchmark performance of StyleGAN2 with Differential Augmentation. Experts’ visual inspection showed that while synthetic images had faults that exposed their true origin to the human eye, a sizable portion of them included identifiable surgical instruments. Experiments with object detection showed that augmenting the training data with synthetic microsurgical data improved the mean average precision for detecting tool tips in practice microsurgery datasets by 3%. Future work will include improving the quality of image synthesis and investigating key visual cues in expert assessment of surgical scenes for applications in robust surgical tool detection, bimanual skill evaluation