First Investigation of Deep Learning for Intraoperative Gauze Segmentation in Minimally Invasive Abdominal Surgery

Surgical gauze is an essential part of surgical procedures, which is primarily used for controlling bleeding and absorbing bodily fluids. The post-surgical retention of gauze can lead to serious complications in the patient's health and necessitate additional surgery for gauze removal. In the wake of data scarcity, the research on gauze segmentation on the real-world surgical data remains underexplored. In this work, we investigate the use of deep learning methods for gauze segmentation in robotassisted minimally invasive abdominal surgeries, utilizing an inhouse surgical dataset prepared at a university hospital. The training data reflects a realistic surgical setting and extensive diversity in spatial, morphological, and visual attributes of three different gauze categories. We have investigated prevalently used segmentation architectures, including CNN-based, transformer-based, and hybrid architectures, to provide a proof-of-concept for gauze segmentation in a realistic setting. Besides, we investigate the influence of additional sub-optimally annotated, auto-tracked segmentation masks to address the bottleneck of data scarcity and performance optimization. Our results demonstrate the efficacy of real-world data to counter the main challenge reported by prior works - the trade-off between blood presence and gauze detection. The incorporation of auto-track annotations enables performance enhancements, particularly in generic cases. The integration of effective segmentation approaches will benefit robotguided surgical procedures and various downstream applications by providing a precise delineation of foreign objects, enhancing patient safety and surgical outcomes.