NeuroFlow is presented, a browser-based workflow for quantitative analysis of mouse brain histology that integrates image registration, signal detection, quantification, and visualization within a single interface and operates across major operating systems without additional software installation.
Abstract
Registration of histological sections to a reference atlas is essential for anatomical localization and region-based quantitative analysis. Although established workflows are powerful, image preparation, registration, quantification, and visualization often rely on multiple software packages, some of which require platform-specific installation or locally configured programming environments. Here, we present NeuroFlow, a browser-based workflow for quantitative analysis of mouse brain histology. NeuroFlow integrates image registration, signal detection, quantification, and visualization within a single interface and operates across major operating systems without additional software installation. It supports affine and nonlinear alignment, as well as real-time oblique reslicing of the reference atlas. All processing is performed locally in a desktop browser, without requiring a local Python environment, MATLAB installation, or associated packages and toolboxes, and without uploading images to a remote server. This design preserves user control over data and keeps intermediate results accessible for inspection and review. NeuroFlow is available at https://guangweizhang.com/tool-neuroflow.html.
NeuroInspector is presented, a lightweight, browser-based environment for inspecting HDF5 and NWB files and provides a dedicated, traceable environment for the inspection stage that precedes formal analysis.
Advances in high-throughput mesoscale microscopy and machine learning-based image analysis pipelines have made unbiased whole-brain imaging widely accessible. However, translating the resulting atlas-mapped datasets into biologically meaningful results remains a substantial barrier owing to their sheer magnitude and co...
Ava L. Shipman, Samuel W. Centanni· bioRxiv· 0 citations
This work enables reproducibility of advanced computational MRI methods within a comprehensive end-to-end open-source framework and proves that quantitative MRI methods consisting of acquisition and reconstruction were successfully implemented in BART.
Daniel Mackner, Philip Schaten, Markus Huemer et al.· arXiv.org· 0 citations
Dendrite is a reproducible, open-source biomedical-computing system for developing and evaluating online BCI paradigms, and validate the full system end-to-end on in-house and public BCI datasets, training and updating decoders online while the pipeline runs in real time.
Neuron counting and segmentation in microscopy images of neuronal cultures is a routine and time-consuming task in neuroscience research, traditionally performed through manual inspection or semi-automatic tools and NeuroAdaptTrainer lowers the barrier for non-specialist users to benefit from deep-learning-based segmen...
Daniela Eraso-Casas, Gerard Villarroya-Piqué, E. Serrano-Pertierra et al.· 0 citations
The novel and flexible architecture of BioImageIT is presented, a lightweight open-source workflow management system designed to bridge the gap between advanced computational tools and end-user bio-image analysts, built upon Python.
Arthur Masson, Sylvain Prigent, C. A. Valades-Cruz et al.· Journal of Microscopy· 0 citations
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