Phan, A. T. Q. | Search bioRxiv

bioRxiv2026

MurineCyto-Det: A High-Resolution Murine BALF Cytology Dataset for Leukocyte Segmentation and Detection

Automated analysis of murine bronchoalveolar lavage fluid (BALF) cytology is important for preclinical respiratory research, yet progress has been limited by the lack of publicly available, well-annotated mouse BALF image datasets. We present MurineCyto-Det, a high-resolution murine BALF cytology dataset comprising 333 image tiles of size 1024x1024 pixels, annotated across five cytological categories with both pixel-level segmentation masks and one-to-one matched bounding boxes. The dataset contains 14,551 annotated cell instances and supports two complementary analysis tasks: morphology-oriented cell segmentation and object-level cell detection. To establish reproducible benchmark baselines, we evaluated representative segmentation and detection models. The results demonstrate the practical utility of MurineCyto-Det while highlighting realistic challenges arising from class imbalance, small object size, irregular cell morphology, and ambiguous debris-like structures. MurineCyto-Det provides a standardized resource for developing, evaluating, and comparing automated methods for murine BALF cytology analysis. The dataset is publicly available at https://doi.org/10.5281/zenodo.17608677.

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bioRxiv2025

denim: an R package for deterministic compartmental models with flexible dwell time distributions

O_LICompartmental models are widely used for dynamical systems where states are discrete such as in infectious diseases epidemiology with the so-called SIR or Susceptible-Infectious-Recovered framework. For mathematical simplicity, rates of transition between compartments are generally assumed to be independent of the dwell time (or secondary time scale in survival analysis): they are either constant or dependent on the epidemiological time (or primary time scale in survival analysis) only, either directly (e.g. environmental or behavioral forcings in epidemiological models) or indirectly through dependence on other variables of the system (e.g. the force of infection in epidemiological models). In some domains of application, this memoryless assumption leads to distributions of dwelling times that are incompatible with those observed on data which can lead to serious problems since the model predictions are highly sensitive on the exact shape of these distributions. C_LIO_LIHere we propose a deterministic, continuous-variable, numerical modelling approach that allows full flexibility on the dwell time distributions. The accompanying denim package provides a user-friendly interface to implement our proposed method through a dedicated language for model definition. C_LIO_LIThe package is open-source and available on CRAN. With more detailed data on the clinical process of infections becoming available, the denim package will be extremely useful for building more realistic epidemiological models providing more accurate projections. C_LI The denim package is publicly available for download on CRAN (https://cran.r-project.org/web/packages/denim/index.html) and GitHub (https://github.com/thinhong/denim). Additional documentation can be found on the denim webpage (https://drthinhong.com/denim/). Bug reports can be submitted via Github Issue.

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