  Acquiring surgical data for research and development is significantly
hindered by high annotation costs and practical and ethical constraints.
Utilizing synthetically generated images could offer a valuable alternative. In
this work, we explore adapting text-to-image generative models for the surgical
domain using the CholecT50 dataset, which provides surgical images annotated
with action triplets (instrument, verb, target). We investigate several
language models and find T5 to offer more distinct features for differentiating
surgical actions on triplet-based textual inputs, and showcasing stronger
alignment between long and triplet-based captions. To address challenges in
training text-to-image models solely on triplet-based captions without
additional inputs and supervisory signals, we discover that triplet text
embeddings are instrument-centric in the latent space. Leveraging this insight,
we design an instrument-based class balancing technique to counteract data
imbalance and skewness, improving training convergence. Extending Imagen, a
diffusion-based generative model, we develop Surgical Imagen to generate
photorealistic and activity-aligned surgical images from triplet-based textual
prompts. We assess the model on quality, alignment, reasoning, and knowledge,
achieving FID and CLIP scores of 3.7 and 26.8% respectively. Human expert
survey shows that participants were highly challenged by the realistic
characteristics of the generated samples, demonstrating Surgical Imagen's
effectiveness as a practical alternative to real data collection.
