Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an observation (such as bleeding). A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs. The objective of this work is to estimate the current state of the surgical task based on the actions performed or events occurred as the task progresses. We propose Fusion-KVE, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events. Additionally, we examine the strengths and weaknesses of different state estimation models in segmenting states with different representative features or levels of granularity. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging, created using the da Vinci® Xi surgical system. Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models in both JIGSAWS suturing dataset and our RIOUS dataset.

Fogged surgical field visualization that is a common and potentially harmful problem can lead to inappropriate device use and incorrectly targeted tissue and increase surgical risks in endoscopic surgery. This paper aims to remove fog or smoke on endoscopic video sequences to augment and maintain a direct and clear visualization of the operating field. A new visibility-driven fusion defogging framework is proposed for surgical endoscopic video processing. This framework first recovers the visibility and enhances the contrast of hazy images. To address the color infidelity problem introduced by the visibility recovery, the luminances of the recovered and enhanced images are fused in the gradient domain, and the fused luminance is reconstructed by solving the Poisson equation in the frequency domain. The proposed method is evaluated on clinical videos that were collected from prostate cancer surgery. The experimental results demonstrate that the proposed framework defogs endoscopic images more robustly than currently available methods. Additionally, our method also provides an effective way to improve the visual quality of medical or high-dynamic range images.

Stereo correspondence is a traditional but still challenging problem in various computer vision tasks. Although current stereo matching algorithms work well, they are still limited by occlusions, texture less and blurred structures, and particularly illumination differences. By revisiting the cost construction and aggregation step in the stereo correspondence procedure, this paper studies a multiscale retinex aggregation method to achieve accurate dense stereo matching. Our method employs the retinex theory to effectively enhance local contrast and utilize color information to boost the matching cost construction and aggregation. We evaluate our proposed framework on benchmark and surgical stereo data. The experimental results demonstrate that our multiscale retinex aggregation provides a more or comparable accurate dense stereo matching strategy. In particular, our method is robust to heavy illumination differences while giving similar performance to state-of-the-art methods on images with uniform illumination.