The New England Journal of Statistics in Data Science

Background: Generalized estimating equations (GEE) and mixed-model repeated measures (MMRM) can handle longitudinal continuous outcomes when modeling the mean with time included categorically. Due to small sample sizes in rare diseases, a compound symmetry (CS) covariance pattern is sometimes adopted. In this setting, there is scant literature in the rare disease community that provide practical advice about the use of both methods based on real datasets from trials conducted in rare diseases, including when to use the sandwich variance estimator with or without a bias correction.

Cell boundary information is crucial for analyzing cell behaviors from time-lapse microscopy videos. Existing supervised cell segmentation tools, such as ImageJ, require tuning various parameters and rely on restrictive assumptions about the shape of the objects. While recent supervised segmentation tools based on convolutional neural networks enhance accuracy, they depend on high-quality labeled images, making them unsuitable for segmenting new types of objects not in the database. We developed a novel unsupervised cell segmentation algorithm based on fast Gaussian processes for noisy microscopy images without the need for parameter tuning or restrictive assumptions about the shape of the object. We derived robust thresholding criteria 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 the alternatives.