A5034437528- Surgical Simulation and Training
- Anatomy and Medical Technology
- Colorectal Cancer Screening and Detection
- Robotics and Sensor-Based Localization
- Artificial Intelligence in Healthcare and Education
- Augmented Reality Applications
- Radiomics and Machine Learning in Medical Imaging
- Colorectal Cancer Surgical Treatments
- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
- Generative Adversarial Networks and Image Synthesis
- Advanced Image and Video Retrieval Techniques
- AI in cancer detection
- Soft Robotics and Applications
- Optical Coherence Tomography Applications
- Cardiac, Anesthesia and Surgical Outcomes
- Nasal Surgery and Airway Studies
- Ear Surgery and Otitis Media
- Advanced X-ray and CT Imaging
- Machine Learning in Healthcare
- Reservoir Engineering and Simulation Methods
- Image Retrieval and Classification Techniques
- Advanced Radiotherapy Techniques
- Advanced Vision and Imaging
- 3D Shape Modeling and Analysis
National Center for Tumor Diseases
2017-2025
TU Dresden
2020-2025
Fresenius (Germany)
2024
Krankenhaus Salem
2023
Chirurgische Universitätsklinik Heidelberg
2023
Heidelberg University
2020-2023
University Hospital Heidelberg
2023
Muroran Institute of Technology
2023
German Cancer Research Center
2020-2023
Nationales Centrum für Tumorerkrankungen Dresden
2021-2023
Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase safety operation through context-sensitive warnings semi-autonomous robotic or improve training surgeons via data-driven feedback. In up to 91% average precision has been reported phase recognition on an open data single-center video dataset. this work we investigated generalizability algorithms in a multicenter setting including more...
In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO KITTI have helped drive enormous improvements by enabling researchers to understand the strengths limitations of different algorithms via performance comparison. However, this type approach has had limited translation problems in robotic assisted surgery field never established same level common benchmarking methods. 2015 a sub-challenge was introduced at EndoVis workshop where set images were provided...
Intra-operative imaging techniques for obtaining the shape and morphology of soft-tissue surfaces in vivo are a key enabling technology advanced surgical systems. Different optical 3-D surface reconstruction laparoscopy have been proposed, however, so far no quantitative comparative validation has performed. Furthermore, robustness methods to clinically important factors like smoke or bleeding not yet assessed. To address these issues, we formed joint international initiative with aim...
In 2015 we began a sub-challenge at the EndoVis workshop MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, limited background variation simple motion rendered dataset uninformative learning about which techniques would be suitable for segmentation real surgery. 2017, same Quebec introduced robotic 10 teams participating challenge to perform binary, articulating parts type da...
Abstract Image-based tracking of medical instruments is an integral part surgical data science applications. Previous research has addressed the tasks detecting, segmenting and based on laparoscopic video data. However, proposed methods still tend to fail when applied challenging images do not generalize well they have been trained on. This paper introduces Heidelberg Colorectal (HeiCo) set - first publicly available enabling comprehensive benchmarking instrument detection segmentation...
Abstract Laparoscopy is an imaging technique that enables minimally-invasive procedures in various medical disciplines including abdominal surgery, gynaecology and urology. To date, publicly available laparoscopic image datasets are mostly limited to general classifications of data, semantic segmentations surgical instruments low-volume weak annotations specific organs. The Dresden Surgical Anatomy Dataset provides eight organs (colon, liver, pancreas, small intestine, spleen, stomach,...
Purpose: In laparoscopic surgery, soft tissue deformations substantially change the surgical site, thus impeding use of preoperative planning during intraoperative navigation. Extracting depth information from endoscopic images and building a surface model field-of-view is one way to represent this constantly deforming environment. The can then be used for registration. Stereo reconstruction typical problem within computer vision. However, most available methods do not fulfill specific...
Purpose: Soft‐tissue deformations can severely degrade the validity of preoperative planning data during computer assisted interventions. Intraoperative imaging such as stereo endoscopic, time‐of‐flight or, laser range scanner be used to compensate these movements. In this context, intraoperative surface has matched model. The shape matching is especially challenging in setting due noisy sensor data, only partially visible surfaces, ambiguous descriptors, and real‐time requirements. Methods:...
Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods detecting, segmenting medical based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, reliable performance state-of-the-art when run challenging (e.g. presence blood, smoke or motion artifacts). Secondly, generalization; algorithms trained specific intervention...
Complex oncological procedures pose various surgical challenges including dissection in distinct tissue planes and preservation of vulnerable anatomical structures throughout different phases. In rectal surgery, violation increases the risk local recurrence autonomous nerve damage resulting incontinence sexual dysfunction. This work explores feasibility phase recognition target structure segmentation robot-assisted resection (RARR) using machine learning.A total 57 RARR were recorded subsets...
Providing the surgeon with right assistance at time during minimally-invasive surgery requires computer-assisted systems to perceive and understand current surgical scene. This can be achieved by analyzing endoscopic image stream. However, images often contain artifacts, such as specular highlights, which hinder further processing steps, e.g., stereo reconstruction, segmentation, visual instrument tracking. Hence, correcting them is a necessary preprocessing step. In this paper, we propose...
Intraoperative segmentation and tracking of minimally invasive instruments is a prerequisite for computer- robotic-assisted surgery. Since additional hardware like systems or the robot encoders are cumbersome lack accuracy, surgical vision evolving as promising techniques to segment track using only endoscopic images. However, what missing so far common image data sets consistent evaluation benchmarking algorithms against each other. The paper presents comparative validation study different...