Author name cluster

Mathias Maier

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9 papers

2 author rows

ICRA Conference 2025 Conference Paper

Intraoperative Trocar-Based Eyeball Rotation Estimation Using Only 2D Microscope Images

Junjie Yang 0001
Satoshi Inagaki
Zhihao Zhao
Daniel Zapp
Mathias Maier
Peter C. Issa
Kai Huang 0001
Nassir Navab

In ophthalmic surgery, surgeons or robots manipulate a light probe and an instrument around two separated trocars following sclerotomy to achieve orbital control for eyeball pose adjustment and subsequent surgical tasks referring to microscope frames. However, current methods face significant challenges in directly extracting the eyeball pose from real-time microscope frames due to the limited microscope perspective and the darkened operating room (OR). This paper decomposes eyeball rotations only along the x and y axes. Then, a method of calculating eyeball poses using eyeball geometry and microscopic trocar positions is presented. This method is tested by simulation and a phantom system with current [2. 0, 2. 8] degree error, providing assistant intraoperative eyeball status in the dark OR with extended method discussions.

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ICRA Conference 2024 Conference Paper

Analyzing Accessibility in Robot-Assisted Vitreoretinal Surgery: Integrating Eye Posture and Robot Position

Satoshi Inagaki
Alireza Alikhani
Nassir Navab
Mathias Maier
M. Ali Nasseri

Several robotic frameworks have been recently developed to assist ophthalmic surgeons in performing complex vitreoretinal procedures such as subretinal injection. However, in order to intuitively integrate robots into the surgical workflow, it is crucial to emphasize that an accessibility analysis framework for vitreoretinal surgery must be considered as an essential component. Such a framework, ideally, considers the comprehensive factors of the eye anatomy and its positioning, the insertion point, and the initial pose and position of the robot. By combining the mobilization of the eyeball and adjusting the pose and position of the robot, the accessibility of such systems is significantly optimized. At the same time, the accessible-visible area is better and faster matched to the working volume of the robot. This paper presents an analysis of an expansion strategy for the robot’s accessibility and visibility area. The outcomes of this method demonstrate the promising potential to enhance the robot’s accessibility, as evidenced in our analytical and experimental findings from 22. 4% to 99. 0% of the required working area on an adjustable phantom model.

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ICRA Conference 2024 Conference Paper

Envibroscope: Real-Time Monitoring and Prediction of Environmental Motion for Enhancing Safety in Robot-Assisted Microsurgery

Alireza Alikhani
Satoshi Inagaki
Shervin Dehghani
Mathias Maier
Nassir Navab
M. Ali Nasseri

Several robotic systems have emerged in the recent past to enhance the precision of micro-surgeries such as retinal procedures. Significant advancements have recently been achieved to increase the precision of such systems beyond surgeon capabilities. However, little attention has been paid to the impact of non-predicted and sudden movements of the patient and the environment. Therefore, analyzing environmental motion and vibrations is crucial to ensuring the optimal performance and reliability of medical systems that require micron-level precision, especially in real-life scenarios. To address this challenge, this paper introduces a novel environmental motion analysis system that employs a grid layout with distributed sensing nodes throughout the environment. This system effectively tracks undesired movements (motions) at designated locations and predicts upcoming motions using neural network-based approaches. The outcomes of our experiments exhibit promising prospects for real-time motion monitoring and prediction, which has the potential to form a solid basis for enhancing the automation, safety, integration, and overall efficiency of robot-assisted micro-surgeries.

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IROS Conference 2024 Conference Paper

Intraocular Reflection Modeling and Avoidance Planning in Image-Guided Ophthalmic Surgeries

Junjie Yang 0001
Zhihao Zhao
Yinzheng Zhao
Daniel Zapp
Mathias Maier
Kai Huang 0001
Nassir Navab
M. Ali Nasseri

Intuitive enhancement of surgical precision in robotic retinal surgery highly depends on the stable acquisition of intraocular imaging data. Such acquisition requires segmenting intraocular components, especially instrument-tip positions, to achieve state estimation and subsequent navigation and motion control. However, intraocular light reflections and glares significantly impact instrument segmentation, state estimation, and subsequent visual servoing in retinal surgery. At the same time, light reflections are among the sources of information for intraoperative navigation. In this work, we propose a method for modeling and optimizing light reflections using microscopy as the standard surgical imaging modality. Beyond optimization, our approach seamlessly integrates the optimized reflection with path planning, strategically circumventing reflection areas and ensuring uninterrupted visibility of instrument tips throughout the surgical procedure. Experiments demonstrate the methodology’s efficacy in avoiding glare affections during eye surgeries.

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IROS Conference 2024 Conference Paper

Shadow Maintenance for Automatic Light-Probe Control in Ophthalmic Surgeries Using Only 2D information

Junjie Yang 0001
Satoshi Inagaki
Zhihao Zhao
Daniel Zapp
Mathias Maier
Kai Huang 0001
Nassir Navab
M. Ali Nasseri

In ophthalmic surgeries, the light probe is responsible for providing safe intraocular illumination and ensuring the visibility of the instrument and its shadow as the only available reference for qualitative depth estimation and landing point prediction in fundus microscopic images. To achieve sustainable shadow-based estimation during surgeries, we propose controlling the light probe automatically to limit the shadow position around the instrument tip using only 2D information from the microscope. We also integrate an intensity balancing sub-module to guarantee the normal intensity distribution and the safe depth of light-tip placement. Without motor-based pose coordination between the light probe and the instrument, experiments analyze the performance of our image-based shadow maintenance with only image information under the constraints of RCM and discuss the working volume and segmentation limitations during simulation and real-robot tests.

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ICRA Conference 2024 Conference Paper

Shadow-Based 3D Pose Estimation of Intraocular Instrument Using Only 2D Images

Junjie Yang 0001
Zhihao Zhao
Mathias Maier
Kai Huang 0001
Nassir Navab
M. Ali Nasseri

In ophthalmic surgeries, such as vitreoretinal operations, surgeons rely on imaging systems, primarily microscopes, for real-time instrument monitoring and motion planning. However, novice surgeons struggle to extract 3D instrument positions from 2D microscope frames, necessitating extensive trial-and-error experience with the background that additional imaging modalities such as iOCT remain inaccessible in most operating rooms. Targeting intraocular assessment within the current surgical setup, this paper presents an imagebased pose estimation method to obtain real-time instrument tip positions in a standard 12mm-radius spherical eyeball model, which links floating instruments with on-the-retinal objects based on the intraocular shadowing principle. We validate this estimation method in a Unity simulator and verify its depth estimation capability using a specially designed eyeball phantom. Both simulator and phantom experiments demonstrate an average needle-tip estimation error within [1. 0, 2. 0] mm using only 2D microscope frames.

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JBHI Journal 2020 Journal Article

Machine Learning Techniques for Ophthalmic Data Processing: A Review

Mhd Hasan Sarhan
M. Ali Nasseri
Daniel Zapp
Mathias Maier
Chris P. Lohmann
Nassir Navab
Abouzar Eslami

Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approaches proposed for diagnosing ophthalmic diseases during the last four years. Three diseases are addressed in this survey, namely diabetic retinopathy, age-related macular degeneration, and glaucoma. The review covers over 60 publications and 25 public datasets and challenges related to the detection, grading, and lesion segmentation of the three considered diseases. Each section provides a summary of the public datasets and challenges related to each pathology and the current methods that have been applied to the problem. Furthermore, the recent machine learning approaches used for retinal vessels segmentation, and methods of retinal layers and fluid segmentation are reviewed. Two main imaging modalities are considered in this survey, namely color fundus imaging, and optical coherence tomography. Machine learning approaches that use eye measurements and visual field data for glaucoma detection are also included in the survey. Finally, the authors provide their views, expectations and the limitations of the future of these techniques in the clinical practice.

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ICRA Conference 2019 Conference Paper

Needle Localization for Robot-assisted Subretinal Injection based on Deep Learning

Mingchuan Zhou
Xijia Wang
Jakob Weiss
Abouzar Eslami
Kai Huang 0001
Mathias Maier
Chris P. Lohmann
Nassir Navab

Subretinal injection is known to be a complicated task for ophthalmologists to perform, the main sources of difficulties are the fine anatomy of the retina, insufficient visual feedback, and high surgical precision. Image guided robot-assisted surgery is one of the promising solutions that bring significant surgical enhancement in treatment outcome and reduces the physical limitations of human surgeons. In this paper, we demonstrate a robust framework for needle detection and localization in subretinal injection using microscope-integrated Optical Coherence Tomography (MI-OCT) based on deep learning. The proposed method consists of two main steps: a) the preprocessing of OCT volumetric images; b) needle localization in the processed images. The first step is to coarsely localize the needle position based on the needle information above the retinal surface and crop the original image into a small region of interest (ROI). Afterward, the cropped small image is fed into a well trained network for detection and localization of the needle segment. The entire framework is extensively validated in ex-vivo pig eye experiments with robotic subretinal injection. The results show that the proposed method can localize the needle accurately with a confidence of 99. 2%.

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ICRA Conference 2018 Conference Paper

Precision Needle Tip Localization Using Optical Coherence Tomography Images for Subretinal Injection

Mingchuan Zhou
Kai Huang 0001
Abouzar Eslami
Hessam Roodaki
Daniel Zapp
Mathias Maier
Chris P. Lohmann
Alois C. Knoll

Subretinal injection is a delicate and complex microsurgery, which requires surgeons to inject the therapeutic substance in a pre-operatively defined and intra-operatively updated subretinal target area. Due to the lack of subretinal visual feedback, it is hard to sense the insertion depth during the procedure, thus affecting the results of surgical outcome and hindering the widespread use of this treatment. This paper presents a novel approach to estimate the 3D position of the needle under the retina using the information from microscope-integrated Intraoperative Optical Coherence Tomography (iOCT). We evaluated our approach on both tissue phantom and ex-vivo porcine eyes. Evaluation results show that the average error in distance measurement is 4. 7 μm (maximum of 16. 5 μm). We furthermore, verified the feasibility of the proposed method to track the insertion depth of needle in robot-assisted subretinal injection.

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