C. C. Reyes-Aldasoro

The paper evaluates training data, ground truth, and shape variability in HeLa cell segmentation using electron microscopy, finding that larger training sets improve accuracy but single-cell models struggle with complex shapes.

This paper describes an unsupervised method for volumetric semantic segmentation of HeLa cell plasma membranes using serial block face scanning electron microscopy data.

This paper presents an algorithm for automatically analyzing honeybees' waggle dance from video data, detecting 44,530 events with 90% tracking accuracy.

This paper compares machine learning algorithms for estimating residual tumour cellularity in breast cancer treated with neoadjuvant therapy, finding Support Vector Machines to be the most accurate.

The paper compares geometric features of nuclear envelopes in wild-type and Chlamydia trachomatis-infected HeLa cells using electron microscopy and spheroid modeling.

The paper estimates cellularity in neo-adjuvant treated breast cancer histological images using morphological features and finds strong correlations between specific parameters and residual tumour cellularity.

This paper compares contact-repulsion behavior in control and Shot3 mutant macrophages using a novel interaction detection framework called macrosight.

This paper compares a traditional image-processing algorithm with three deep learning models for semantic segmentation of HeLa cells, finding the traditional method outperforms all deep learning architectures.

The paper introduces Cell Tracking Profiler, a framework for analyzing 4D live cell imaging data, and demonstrates its use in studying muscle stem cell behavior during regeneration in zebrafish.

The paper describes β-glucan-dependent conidial shuttling between neutrophils and macrophages during fungal infection, enhancing conidial survival and infection establishment.