One step surgical scene restoration for robot assisted minimally invasive surgery

Abstract

Minimally invasive surgery (MIS) offers several advantages to patients including minimum blood loss and quick recovery time. However, lack of tactile or haptic feedback and poor visualization of the surgical site often result in some unintentional tissue damage. Visualization aspects further limits the collection of imaged frame contextual details, therefore the utility of computational methods such as tracking of tissue and tools, scene segmentation, and depth estimation are of paramount interest. Here, we discuss an online preprocessing framework that overcomes routinely encountered visualization challenges associated with the MIS. We resolve three pivotal surgical scene reconstruction tasks in a single step; namely, (i) denoise, (ii) deblur, and (iii) color correction. Our proposed method provides a latent clean and sharp image in the standard RGB color space from its noisy, blurred, and raw inputs in a single preprocessing step (end-to-end in one step). The proposed approach is compared against current state-of-the-art methods that perform each of the image restoration tasks separately. Results from knee arthroscopy show that our method outperforms existing solutions in tackling high-level vision tasks at a significantly reduced computation time.

Clinical applications of artificial intelligence in robotic surgery

“…To combat these changes, Ali et al have developed an online preprocessing framework capable of denoising, deblurring, and color-correcting real-time camera imaging to enhance intraoperative visualization in knee arthroscopy. Their method outperformed existing image enhancement with significantly reduced computation time to image display [ 5 ]. Robotic surgeons also often utilize electrocautery devices for dissection and ligation tasks, but this process generates smoke which remains trapped in the anatomical space of interest which can temporarily obscure the visual field until suction removal.…”