Erik H. W. Meijering

This paper introduces SemaPlex, a method that enhances multiplexed fluorescence imaging by semantically unmixing markers, enabling higher plexity and accurate cell phenotype classification.

The paper shows that image quality metrics (IQMs) are unreliable for guiding fluorescence microscopy image processing as they do not correlate with downstream biological analysis performance.

BigNeuron benchmarks automatic neuron reconstruction algorithms using gold standard annotations and image quality metrics, showing that consensus methods outperform individual algorithms.

The paper addresses imbalanced multilabel classification for protein subcellular localization using oversampling techniques to improve minority class prediction performance.

This paper proposes a method to improve classification of imbalanced blood cell images using data augmentation techniques and a minority class sampling strategy, achieving better performance on minority classes.

This paper introduces a 3D texture descriptor for nuclear classification and phenotypic state characterization using chromatin patterns in human cells.

The paper uses a CRISPR/Cas9 screen in zebrafish to identify Il34 as a factor facilitating yolk sac macrophage distribution and brain colonization.

UltraTracer extends neuron-tracing algorithms to handle ultra-large image datasets, achieving scalability and efficiency improvements over existing methods.