A5060719302- Cell Image Analysis Techniques
- Image Processing Techniques and Applications
- AI in cancer detection
- Medical Image Segmentation Techniques
- Advanced Fluorescence Microscopy Techniques
- Generative Adversarial Networks and Image Synthesis
- Single-cell and spatial transcriptomics
- Radiomics and Machine Learning in Medical Imaging
- Remote Sensing and LiDAR Applications
- Smart Agriculture and AI
- Medical Imaging Techniques and Applications
- Photoacoustic and Ultrasonic Imaging
- Retinal Imaging and Analysis
- Colorectal Cancer Screening and Detection
- Advanced Vision and Imaging
- Pregnancy and preeclampsia studies
- 3D Shape Modeling and Analysis
- Skin and Cellular Biology Research
- Dermatological and Skeletal Disorders
- Maternal and fetal healthcare
- Reproductive System and Pregnancy
- Developmental Biology and Gene Regulation
- Domain Adaptation and Few-Shot Learning
- Advanced Neural Network Applications
- Face and Expression Recognition
RWTH Aachen University
2016-2024
Generalized nucleus segmentation techniques can contribute greatly to reducing the time develop and validate visual biomarkers for new digital pathology datasets. We summarize results of MoNuSeg 2018 Challenge whose objective was generalizable nuclei in pathology. The challenge an official satellite event MICCAI conference which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set 30 images seven organs annotations...
Positional information is a central concept in developmental biology. In developing organs, positional can be idealized as local coordinate system that arises from morphogen gradients controlled by organizers at key locations. This offers plausible mechanism for the integration of molecular networks operating individual cells into spatially coordinated multicellular responses necessary organization emergent forms. Understanding how cues guide morphogenesis requires quantification gene...
Recent advances in computer vision have led to significant progress the generation of realistic image data, with denoising diffusion probabilistic models proving be a particularly effective method. In this study, we demonstrate that can effectively generate fully-annotated microscopy data sets through an unsupervised and intuitive approach, using rough sketches desired structures as starting point. The proposed pipeline helps reduce reliance on manual annotations when training deep...
The quantitative analysis of cellular membranes helps understanding developmental processes at the level. Particularly 3D microscopic image data offers valuable insights into cell dynamics, but error-free automatic segmentation remains challenging due to huge amount generated and strong variations in intensities. In this paper, we propose a new approach that combines discriminative power convolutional neural networks (CNNs) for preprocessing investigates performance three watershed-based...
Increasing data set sizes of 3D microscopy imaging experiments demand for an automation segmentation processes to be able extract meaningful biomedical information. Due the shortage annotated image that can used machine learning-based approaches, approaches are required robust and generalize well unseen data. The Cellpose approach proposed by Stringer et al. [1] proved such a generalist cell instance tasks. In this paper, we extend improve accuracy on further show how formulation gradient...
Pre-eclampsia is a severe placenta-related complication of pregnancy with limited early diagnostic and therapeutic options. Aetiological knowledge controversial, there no universal consensus on what constitutes the late phenotypes pre-eclampsia. Phenotyping native placental three-dimensional (3D) morphology offers novel approach to improve our understanding structural abnormalities in Healthy pre-eclamptic tissues were imaged multiphoton microscopy (MPM). Imaging based inherent signal...
Cell division, or mitosis, guarantees the accurate inheritance of genomic information kept in cell nucleus. Malfunctions this process cause a threat to health and life organism, including cancer other manifold diseases. It is therefore crucial study detail cell-cycle general mitosis particular. Consequently, large number manual semi-automated time-lapse microscopy image analyses have been carried out recent years. In paper, we propose method for automatic detection stages using recurrent...
Automated image processing approaches are indispensable for many biomedical experiments and help to cope with the increasing amount of microscopy data in a fast reproducible way. Especially state-of-the-art deep learning-based most often require large amounts annotated training produce accurate generalist outputs, but they compromised by general lack those sets. In this work, we propose how conditional generative adversarial networks can be utilized generate realistic 3D fluorescence from...
Recent microscopy imaging techniques allow to precisely analyze cell morphology in 3D image data. To process the vast amount of data generated by current digitized techniques, automated approaches are demanded more than ever. Segmentation used for morphological analyses, however, often prone produce unnaturally shaped predictions, which conclusion could lead inaccurate experimental outcomes. In order minimize further manual interaction, shape priors help constrain predictions set natural...
Abstract Positional information is a central concept in developmental biology. In developing organs, positional can be idealized as local coordinate system that arises from morphogen gradients controlled by organizers at key locations. This offers plausible mechanism for the integration of molecular networks operating individual cells into spatially-coordinated multicellular responses necessary organization emergent forms. Understanding how cues guide morphogenesis requires quantification...
Recent advances in computer vision have led to significant progress the generation of realistic image data, with denoising diffusion probabilistic models proving be a particularly effective method. In this study, we demonstrate that can effectively generate fully-annotated microscopy data sets through an unsupervised and intuitive approach, using rough sketches desired structures as starting point. The proposed pipeline helps reduce reliance on manual annotations when training deep...
ABSTRACT Cell division, or mitosis, guarantees the accurate inheritance of genomic information kept in cell nucleus. Malfunctions this process cause a threat to health and life organism, including cancer other manifold diseases. It is therefore crucial study detail cell-cycle general mitosis particular. Consequently, large number manual semi-automated time-lapse microscopy image analyses have been carried out recent years. In paper, we propose method for automatic detection stages using...
Automatic analysis of spatio-temporal microscopy images is inevitable for state-of-the-art research in the life sciences. Recent developments deep learning provide powerful tools automatic analyses such image data, but heavily depend on amount and quality provided training data to perform well. To this end, we developed a new method realistic generation synthetic 2D+t fluorescently labeled cellular nuclei. The combines spatiotemporal statistical shape models different cell cycle stages with...
The segmentation and tracking of living cells play a vital role within the biomedical domain, particularly in cancer research, drug development, developmental biology. These are usually tedious time-consuming tasks that traditionally done by experts. Recently, to automatize these processes, deep learning based methods have been proposed. require large-scale datasets their full potential is constrained scarcity annotated data imaging domain. To address this limitation, we propose Biomedical...
Increasing data set sizes of 3D microscopy imaging experiments demand for an automation segmentation processes to be able extract meaningful biomedical information. Due the shortage annotated image that can used machine learning-based approaches, approaches are required robust and generalize well unseen data. The Cellpose approach proposed by Stringer et al. proved such a generalist cell instance tasks. In this paper, we extend improve accuracy on further show how formulation gradient maps...
The quantitative analysis of cellular membranes helps understanding developmental processes at the level. Particularly 3D microscopic image data offers valuable insights into cell dynamics, but error-free automatic segmentation remains challenging due to huge amount generated and strong variations in intensities. In this paper, we propose a new approach which combines discriminative power convolutional neural networks (CNNs) for preprocessing investigates performance three watershed-based...