Author name cluster

Daniel Zapp

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 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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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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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 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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