Table 3.
Challenges and potential solutions to increase throughput of imaging techniques
| Challenge | Type | Potential solutions |
|---|---|---|
| Sample preparation | 2D | Development of predictive screening assays (→ use of primary or embryonic stem cells (ESCs) instead of easy to culture tumour cell-lines but genetically aberrant) |
| 3D | Development and validation of relevant 3D scaffolds (→ characterise ECM of patient tumour material) | |
| Improve 3D cell culture techniques for automated liquid handling robotics (→ collaboration between academia and pharmaceutical companies) | ||
| In vivo | Automated microinjection of tumour cells in ZF (→ automatic microinjector based on pattern recognition) | |
| Automated filling of the microwells plates with ZF preferably all similarly orientated (→ make use of adapted mould) | ||
| Automated image acquisition | 2D | Autofocus combined with z-scans for 3D imaging (→ image-based or reflection-based autofocusing) |
| Pre-optimisation of the acquisition settings (→ autoexposure algorithm to adjust integration time of detectors) | ||
| Automated object (cells of matrix adhesion) localisation (→ autoexposure algorithm to adjust integration time of detectors) | ||
| Intelligent microscope [158, 172] | ||
| 3D/in vivo | Higher throughput kinetic imaging microscopes suitable for automated 3D invasion studies (→ see commercially available kinetic imaging systems such as Incucyte or Cell-IQ [117]) | |
| Higher throughput kinetic imaging microscopes suitable for FRET, FRAP or FCS (→ in the future, intelligent microscope that recognises the object to be visualised) | ||
| Data handling | 2D | Storing terabytes of data (→ storage area network (SAN) which has multiterabyte to tens of terabytes capacity; commonly, data on the SAN are backed up on tape as well) |
| 3D/in vivo | ||
| Data management (→ development of databases retrieved [157, Table 2]) | ||
| Image analysis | 2D | Image segmentation (→ depending on imaging quality, choose between region-based, edge-based or region-growing method) |
| 3D/in vivo | ||
| Multiparametric image analysis (→ phenotypic profiling which involves computer vision methods) | ||
| Object tracking (→ high time resolution for imaging; adapted tracking algorithms for 3D imaging [173] and HTS data) | ||
| Data mining and modeling | 2D | Screening reproducibility and estimators (→ quality standards, e.g. coefficient of variations (CVs) and zscores should not exceed 5% and should be higher than 0.5, respectively) |
| 3D/in vivo | Significant behaviour changes detection | |
| Automated classification (→ supervised machine learning) | ||
| Development of computational models |