Statistical and Generative Modeling Approaches for Cell Microscopy Image Segmentation and Synthesis
Abstract
Cell boundary detection and segmentation enable the quantitative analysis of cellular organization, morphology, and dynamics, which are fundamental to understanding biological processes and disease mechanisms. Accurate detection and segmentation from time-lapse microscopy videos are essential for providing longitudinal information of cell boundaries. Popular unsupervised cell segmentation tools, such as ImageJ, require manual tuning of parameters and rely on restrictive assumptions about the shape of the objects. While recently developed supervised segmentation tools based on convolutional neural networks enhance accuracy, they depend on high-quality labeled images for training, making them unsuitable for segmenting new types of objects not in existing databases. To address these limitations, we developed an unsupervised cell segmentation algorithm from noisy microscopy images using fast Gaussian processes without the need for parameter tuning or restrictive assumptions about the shape of the object. We derived robust thresholding criteria that are adaptive for heterogeneous images containing distinct brightness at different parts to separate objects from the background, and employed watershed segmentation to distinguish touching cell objects. Both simulated studies and real-data analysis of large microscopy images demonstrate the scalability and accuracy of our approach compared with existing alternatives. Our method achieves consistently higher average precision scores…
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.