A streamlined software platform that transforms raw imaging data from 3D cancer model assays into clear, quantitative insights with minimal user input
Proposed Uses
3DcX is designed to support researchers working with organoid, spheroid, and cell‑therapy assays by automating the analysis of time‑lapse imaging data.
It enables rapid interpretation of treatment effects, supports screening workflows, and reduces the time and manual effort typically required for 3D assay analysis.
Problem addressed
Analysis of 3D cancer models is often slow, subjective, and fragmented across multiple tools.
Existing software often fails when faced with low‑contrast imaging, late‑stage degradation, or substantial object movement in time‑lapse datasets, and many systems are restricted to proprietary microscope ecosystems.
3DcX overcomes these challenges by replacing manual, bias-prone steps, ensuring stable tracking under poor imaging conditions, and generating reproducible, time‑resolved metrics across diverse imaging systems.
Technology Overview
3DcX provides an integrated environment for processing and analysing time‑lapse datasets from 3D cancer assays. The platform ingests standard image and video formats, identifies relevant structures, assesses their viability, and tracks their behaviour throughout the experiment to generate clear, quantitative and visual outputs.
Designed to cope with movement, low contrast, and structural collapse, it smooths and stabilises lower‑quality inputs to ensure reliable performance. Its microscope‑agnostic design supports a wide range of imaging setups, and the fully automated workflow delivers consistent, experiment‑ready results with minimal user intervention.
Benefits
- Automated, unbiased analysis of 3D assay data
- Compatible with images from any microscope
- Performs reliably across different imaging conditions
- Supports time-based assessment of growth viability, and treatment effects.
- Delivers clear, quantitative metrics and visual outputs
- Suitable for both exploratory and higher throughput workflows
Contact for this technology
Commercialisation Officer, Faculty of Natural Sciences
Emily Russell