AI Microscopy Image Analyst is a microscopy specialist that reviews confocal, TEM, SEM, and histology images to flag artifacts, identify structures, and guide quantification. Unlike generic vision tools that interpret brightness as biology, this agent traces every feature to sample prep, instrument, and acquisition. Built on Jenova, it combines models from OpenAI, Anthropic, Google, and more with tools for literature and figure review. Histology reviews are observational — not a pathologist's diagnosis.
Microscopy Image Analyst begins every review by naming the likely modality and testing whether SNR, saturation, Nyquist sampling, and a visible scale bar can support the question you asked. Missing metadata is listed before any biological or materials claim; a screenshot orients the field, but original acquisition files carry the real pixel data. When quality fails, this agent returns re-acquisition guidance — Nyquist pixel size, gain, coating, focus, or Köhler illumination — instead of forcing a structure call.
Microscopy Image Analyst identifies organelles, grains, biofilms, and IHC patterns only after listing artifacts that could impersonate them. A double membrane enclosing cytoplasm on TEM is treated as consistent with an autophagosome; green signal in a GFP channel is not called specific until autofluorescence and bleed-through are excluded; lattice fringes that will not index are held as possible Moiré until tilt or FFT says otherwise. Confidence is stated explicitly, and claims the modality cannot resolve are refused with the technique that would settle them.
Microscopy Image Analyst designs quantification only after calibration and controls are in place, matching the job to ImageJ/FIJI, CellProfiler, QuPath, Ilastik, napari, or Imaris rather than one default for every image. Pipelines follow preprocessing → segmentation → measurement → export → statistics, with warnings when automatic exposure, uncalibrated scales, saturated pixels, or a single section of a 3D object would make the number indefensible. A 96-well phenotype screen, a FIB-SEM interface measurement, and a Ki-67 whole-slide score do not share a workflow, and a 2D grain-size mean is flagged when it is stereologically biased.
Microscopy Image Analyst reviews manuscript figures for uniform — not regional — contrast, channels shown individually plus the merge, scale bars on every panel, and methods detail sufficient to reproduce the field. The AI separates the representative image from the quantified dataset so the cleanest frame is not mistaken for the measurement. Settings are treated as part of the result and can be exported as a document for the notebook or the submission packet.
| Traditional Approach | Microscopy Image Analyst |
|---|---|
| Hallway consult on the brightest object | Structured report from quality to next steps |
| Threshold first, ask calibration later | Quality gate before any measurement |
| Treat brightness as biology | Flags saturation, charging, knife marks |
| One software habit for every image | Matches FIJI, CellProfiler, QuPath, napari |
Acquisition memory vs. forgotten gains and rejected fields
A generic chatbot will not retain which confocal fields were saturated, which control used a different gain, which SEM frames were already thrown out for charging, or which scale bars failed a prior check. Microscopy Image Analyst keeps those decisions across sessions so an LC3-GFP puncta count is not mixed with a clipped stack, and a particle-size mean is not rebuilt from fields the lab already rejected. Losing that history is how a figure ships with mixed acquisition conditions and an indefensible n.
Model switching from organelle calls to pipeline prose
TEM cross-sections, lattice-versus-Moiré decisions, and refused over-calls need a cautious reasoner; FIJI macro steps and figure checklists need fluent procedural writing. This agent can move among models from OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, and more at those different phases, instead of staying locked to ChatGPT (OpenAI only) or Claude (Anthropic only) for every part of the review. A model that writes smooth methods text is not automatically the right model to decide whether a 200 nm widefield blob is an organelle.
From micrograph to measurement plan in one workflow
Microscopy Image Analyst turns a field plus a one-sentence question into a technical gate, a full-field survey, an artifact list, a software-matched pipeline, and a publication checklist. Original stacks can be pulled from Google Drive or Dropbox, reporting conventions checked with Google Scholar, and the measurement plan exported as a document — so the path is micrograph → quality gate → structure and artifact survey → FIJI, CellProfiler, or QuPath pipeline → figure pass. That is a production review, not a caption on the brightest object.
Modality-aware review instead of brightest-object guessing
Widefield, confocal, STED, STORM/PALM, SIM, TEM, STEM, cryo-EM, SEM, FIB-SEM, AFM, STM, histology, and CLEM do not share one object-detection logic. The AI changes the framework with the frame: organelle survey on biological TEM, diffraction logic on materials TEM, charging and edge-effect checks on SEM, tip-convolution caution on AFM. A 200 nm “vesicle” on a widefield image stays a diffraction-limited blob until a modality that can resolve it is used.
Cell biologists quantifying fluorescence assays
Use this agent when LC3-GFP puncta, colocalization in a three-channel merge, or a confocal Z-projection has to survive the question of saturation, bleed-through, and a documented per-cell normalization.
Materials scientists working with SEM, TEM, and AFM
Use this agent when nanoparticle diameters, lattice fringes, or grain size must be separated from charging, sputter grain, knife marks, projection error, and tip convolution before a distribution is reported.
Core-facility staff and trainees at the instrument
Use this agent between booked slots when a dim preview, striped detector noise, or uncertain gain needs a short re-acquisition list rather than another hour of scanning a bleaching sample.
Histology and pathology researchers (non-diagnostic)
Use this agent to walk H&E architecture, cellularity, and nuclear features and to list processing artifacts before any pattern discussion — for research and teaching, not for a formal diagnosis.
Authors assembling microscopy figures for submission
Use this agent for a last pass on scale bars, individual channels plus the merge, uniform contrast, and the split between the representative field and the quantified dataset.
Free For exploring Jenova:
Plus ($20/month) For individuals:
Premium ($50/month) For regular users:
Pro ($100/month) For power users:
Max ($200/month) For professionals:
Ultra ($500/month) For heavy professionals:
Enterprise ($1,000/month) For businesses:
All plans are billed monthly with no daily usage caps — your full quota is available immediately on each billing date.
Microscopy Image Analyst is an AI that reads a micrograph the way a senior microscopist would — as sample preparation plus instrument plus acquisition settings, not as a pretty picture. The first pass names the modality, tests whether quality can support the question, and lists missing metadata such as a scale bar or pixel size. Structures, artifacts, quantification advice, and figure checks come only after that technical gate.
Microscopy Image Analyst retains acquisition settings, control pairings, rejected fields, and prior artifact calls, so a later puncta count is not mixed with a saturated stack a generic chatbot would have forgotten. You can switch among models from OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, and more — a cautious reasoner for TEM organelle calls, a different model for FIJI pipeline prose — instead of staying locked to ChatGPT or Claude alone. Google Drive, Dropbox, Google Scholar, and document export sit in the same review, so one conversation can carry a field from quality gate to measurement plan to figure checklist.
Microscopy Image Analyst works with Google Drive and Dropbox for original stacks, Google Scholar and Google Search for reporting conventions and methods language, Google Images for visual reference, YouTube for software walkthroughs, and Notion for lab notes, plus document export of the review. The intended chain is upload the field, run the technical gate on real pixels, survey structures and artifacts, then export a FIJI, CellProfiler, or QuPath plan. The AI does not replace those analysis programs; it decides whether a measurement is valid and which program fits the image.
ImageJ/FIJI, CellProfiler, and QuPath measure; Microscopy Image Analyst decides whether a measurement is valid and which program fits the job. FIJI remains the general workbench, CellProfiler is the usual high-throughput phenotyping choice, and QuPath is built for whole-slide pathology. The analyst sends you into the right tool with a pipeline and the controls to lock — it does not become your saved macro.
Coverage includes brightfield and histology, widefield and confocal fluorescence, STORM/PALM, SIM, STED, TEM, STEM, cryo-EM, SEM, FIB-SEM, AFM, STM, and correlative setups such as CLEM. The framework changes with the modality: organelle survey on biological TEM, lattice and diffraction logic on materials TEM, charging and edge-effect checks on SEM. Super-resolution does not turn a prior widefield blob into a confident organelle ID.
No. Histology and cytology reviews describe morphological features and consistency with known patterns for research and education. Formal diagnosis requires a qualified pathologist. The same observational limit applies if the project also involves clinical scans better handled by a radiology-focused specialist.
Reliability is stated on the call. High-confidence labels are reserved for features the modality can actually resolve — a mitochondrion in cross-section on adequate TEM, or a 3+ membranous IHC pattern with visible controls. Ambiguous objects are labeled as consistent with a short list, and unresolvable claims are refused with the modality that would settle them.
Yes. Analysis runs with full feature parity on web, iOS, and Android, including speech-to-text if you would rather dictate voltage, objective, or channel settings at the scope. A phone photo of the display is enough for a first troubleshooting pass; upload the original file when you need accurate pixels. iOS: Jenova on the App Store. Android: Jenova on Google Play.
Yes, for orientation, modality guesses, and obvious problems such as a missing scale bar, striped noise, or a badly clipped preview. Pixel-level saturation, true SNR, and calibrated size are more reliable from the acquisition file. State the question in one sentence — identification, quality, quantification, or figure readiness — so the technical gate knows what the frame must support.
Yes. If sampling, SNR, focus, coating, or a clipped histogram cannot support the claim, the AI lists what you may still say and what must be re-acquired. Typical guidance includes Nyquist pixel size, gain, coating, focus, and Köhler setup, and it will call out automatic exposure as a quantification killer. Forcing a structure ID on an insufficient frame is treated as the failure mode to avoid.
No. The agent recommends software, writes a pipeline-shaped plan, and names what to validate; execution still happens in FIJI, CellProfiler, QuPath, Ilastik, napari, or Imaris. Reproducible analysis depends on saved workflows and locked settings, not on a one-off click path. Use the review to decide what number would be irreproducible before you run the macro.
No. Conversations and uploaded fields are not used to train public models, are encrypted in transit and at rest, and are not sold or shared with advertisers. That matters when the stack is still unpublished or the figure is under embargo. Keep original files in Drive or Dropbox and attach them when you need the review grounded in the real pixels.
Microscopy Image Analyst is part of Jenova, the most powerful AI agent platform in the world. Jenova provides unified access to leading AI models from OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, and more — alongside intelligent tool integrations for end-to-end task completion. The platform features persistent cross-session memory, automated workflow orchestration, and full parity across web, iOS, and Android. Developed by Azeroth Inc., a New York-based technology company, Jenova serves users in over 70 countries.
For developers, the Jenova Agent API provides a fully-managed agent backend with built-in orchestration, multi-model routing, memory, storage, and MCP tool support — replacing the need to stitch together raw LLM APIs, vector databases, and custom tool servers.
Contact: [email protected]