IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering最新文献

Subretinal injection is a critical procedure for delivering therapeutic agents to treat retinal diseases such as inherited retinal diseases (IRD) and age-related macular degeneration (AMD). However, retinal motion caused by physiological factors such as respiration and heartbeat significantly impacts precise needle positioning, increasing the risk of retinal pigment epithelium (RPE) damage. This paper presents a fully autonomous robotic subretinal injection system that integrates intraoperative optical coherence tomography (iOCT) imaging and deep learning-based motion prediction to synchronize needle and retinal motion. A Long Short-Term Memory (LSTM) neural network is used to predict internal limiting membrane (ILM) motion, outperforming a Fast Fourier Transform (FFT)-based baseline model. Additionally, a real-time registration framework aligns the needle tip position with the robot's coordinate frame. Then, a dynamic proportional speed control strategy ensures smooth and adaptive needle insertion. Experimental validation in both simulation and ex vivo open-sky porcine eyes demonstrates precise motion synchronization and successful subretinal injections. The experiments achieve a mean tracking error below 16.4 μm in pre-insertion phases. These results show the potential of AI-driven robotic assistance to improve the safety and accuracy of retinal microsurgery.

This paper presents and evaluates methods of fusing semantic image segmentation predictions, and highlights a novel hybrid approach that combines spatial frequency and edge features. Tool-labeled endoscopy from sinus surgery served as the image dataset, while two methods of surgical tool segmentation via morphological polar transform provided distinct predictions. The morphological transform acted as an input pre-processing step prior to segmentation via the U-Net architecture. Two separate predictions were available for each image based on the transformation center: one at the surgical tool-tip (TT) and one at the surgical tool vanishing point (VP). The goal in this work was to systematically generate a superior segmentation by fusing information from the two aforementioned predictions. Improved segmentation performance in this domain is envisioned to enable vision-based force estimation in robot-assisted minimally invasive surgery (RMIS), where lack of reliable force and tactile feedback has continued to be an ongoing challenge. While methods for deep learning based segmentation fusion exist, such methods require extensive datasets and potentially obfuscate explainability. Thus, three approaches relying solely on low-level features to fuse grayscale segmentation predictions were proposed in this work: (1) gradient estimation, (2) Laplacian pyramid and (3) a modified spatial frequency method. The latter two demonstrated enhanced segmentation compared to original predictions. This work also explores explainability towards identifying candidate prediction pairs for fusion via unsupervised clustering as well as a ResNet-18 model. Cursory investigations into properties of the fused predictions provide insight into the potential use of the proposed methods in domains other than surgical tool segmentation.

This paper presents a probabilistic method for active localization of needle and targets in robotic image guided interventions. Specifically, an active localization scenario where the system directly controls the imaging system to actively localize the needle and target locations using intra-operative medical imaging (e.g., computerized tomography and ultrasound imaging) is explored. In the proposed method, the active localization problem is posed as an information maximization problem, where the beliefs for the needle and target states are represented and estimated using particle filters. The proposed method is also validated using a simulation study.