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

Deep Learning-Enhanced Robotic Subretinal Injection with Real-Time Retinal Motion Compensation. 基于实时视网膜运动补偿的深度学习增强机器人视网膜下注射。

IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering Pub Date : 2025-08-01 Epub Date: 2025-09-23 DOI: 10.1109/case58245.2025.11163942

Tianle Wu, Mojtaba Esfandiari, Peiyao Zhang, Russell H Taylor, Peter Gehlbach, Iulian Iordachita

{"title":"Deep Learning-Enhanced Robotic Subretinal Injection with Real-Time Retinal Motion Compensation.","authors":"Tianle Wu, Mojtaba Esfandiari, Peiyao Zhang, Russell H Taylor, Peter Gehlbach, Iulian Iordachita","doi":"10.1109/case58245.2025.11163942","DOIUrl":"10.1109/case58245.2025.11163942","url":null,"abstract":"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.","PeriodicalId":90520,"journal":{"name":"IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering","volume":"2025 ","pages":"1285-1291"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12459653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fusing Tool Segmentation Predictions from Pose-Informed Morphological Polar Transform of Endoscopic Images. 基于姿态信息的内镜图像形态极变换的融合工具分割预测。

IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering Pub Date : 2025-08-01 Epub Date: 2025-09-23 DOI: 10.1109/case58245.2025.11164078

Xiaoyi Wu, Dina Sehnawi, Yicheng Zhu, Yangming Lee, Kevin Huang

{"title":"Fusing Tool Segmentation Predictions from Pose-Informed Morphological Polar Transform of Endoscopic Images.","authors":"Xiaoyi Wu, Dina Sehnawi, Yicheng Zhu, Yangming Lee, Kevin Huang","doi":"10.1109/case58245.2025.11164078","DOIUrl":"10.1109/case58245.2025.11164078","url":null,"abstract":"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.","PeriodicalId":90520,"journal":{"name":"IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering","volume":"2025 ","pages":"1316-1322"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145851676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Network Simulation of Cardiac Electrical Dynamics. 心脏电动力学优化网络仿真。

IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering Pub Date : 2025-08-01 Epub Date: 2025-09-23 DOI: 10.1109/case58245.2025.11163831

Runsang Liu, Hui Yang

{"title":"Optimizing Network Simulation of Cardiac Electrical Dynamics.","authors":"Runsang Liu, Hui Yang","doi":"10.1109/case58245.2025.11163831","DOIUrl":"10.1109/case58245.2025.11163831","url":null,"abstract":"Modeling and simulation play a critical role in cardiology research. Our recent study has discovered that the structural geometry of a heart can be effectively represented by a network. This, in turn, provides an opportunity to efficiently model and simulate cardiac dynamics using a sparse adjacency matrix. However, realizing the full potential of network simulation is highly dependent on optimization methodologies. The calibration of cardiac models involves substantial complexity. Cardiac electrical dynamics are not only chaotic and nonstationary but also computationally expensive, which poses significant challenges to traditional calibration methodologies. Thus, this paper presents a new statistical metamodeling framework for optimizing network simulation of cardiac electrical dynamics. First, a statistical surrogate is developed to predict the response of the computationally expensive simulation model under different experimental scenarios (i.e., parameter settings). Next, the uncertainty estimate of the statistical metamodel is leveraged to sequentially guide the selection of the next best parameter setting that yields the maximum expected improvement. As such, the optimal parameter setting for cardiac simulation can be efficiently identified through this iterative process. The proposed methodology is evaluated and validated through case studies on both 2D cardiac tissue and a whole heart. Experimental results show that the proposed statistical metamodeling approach efficiently calibrates network simulation of complex spatiotemporal dynamics.","PeriodicalId":90520,"journal":{"name":"IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering","volume":"2025 ","pages":"814-819"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12998970/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147488839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Particle Filter Based Active Localization of Target and Needle in Robotic Image-Guided Intervention Systems. 基于粒子滤波的机器人图像引导干预系统中目标和指针的主动定位。

IEEE International Conference on Automation Science and Engineering (CASE) : [proceedings]. IEEE Conference on Automation Science and Engineering Pub Date : 2013-08-01 DOI: 10.1109/CoASE.2013.6653938

Mark Renfrew, Zhuofu Bai, M Cenk Cavuşoğlu

引用次数: 8