CellCounter 1.0.11 (macOS)

This release lets you import and inspect images before installing a segmentation model, preview analysis settings on a representative image, and review completed results while the rest of a batch processes.

The macOS application requires macOS 15 or later and is distributed as a universal Intel and Apple Silicon build. It is ad-hoc signed and not notarized. See the installation guide for first-launch instructions.

Import, preview and processing

• Import images or folders into a saved analysis setup. Choose the model, calibration, channels, Z projection and preprocessing before running the batch, or import images for inspection without segmentation.
• Preview a whole representative image with an installed model. Matching preview results are reused when the batch starts.
• Start from task presets for cell counts and sizes, nuclei, marker positivity or wound closure. Presets guide the workflow; marker and wound measurements still run in their corresponding assay panels.
• Processing jobs persist locally with their settings, progress and per-image failures. Pause after the current image, resume, retry failures, or open completed images while processing continues. Interrupted jobs return as paused after relaunch.
• Saved run settings distinguish the settings that produced a result from later changes to application defaults.

Review and navigation

• Linked measurement rows, area/intensity plots and image overlays share cell selection, making it easier to inspect unusual measurements.
• Compare saved and current mask variants side by side with synchronized pan and zoom, highlighted changes and a differences-only view.
• Analysis state indicators make processing, saved settings and the need to rerun clearer.
• Contextual menus connect search, selection, export, image navigation, zoom, review, training and processing shortcuts to the visible screen. Text fields retain native selection and undo behavior; unavailable actions are disabled.
• Help → Keyboard Shortcuts (⌘/) and Settings share the shortcut reference. ⌘O opens images, ⌘⇧O opens a folder, and ⌘⌥1 through ⌘⌥9 navigate the primary screens.
• A new microscopy app icon replaces the previous application artwork.

Models and responsiveness

• The Models screen uses cached installation state and asynchronous probes instead of blocking navigation while inspecting Python environments. Slow or failed probes no longer block screen navigation.
• Dense mask overlays use spatial indexing and visible-region drawing. Measurement and summary updates avoid unnecessary decoding and recomputation.
• Assay workers stay warm between compatible requests, with bounded caches for prepared images and masks.
• Processing overlaps image preparation when memory permits, while model execution and training share an execution guard to avoid competing model allocations.

Real local fine-tuning

The macOS fine-tuning workflow now trains from actual reviewed library masks and reports measured held-out evaluation. The annotation view displays the source image and corrected boundaries; saved mask edits become the training labels.

Training uses an installed Cellpose 3.x environment and existing compatible base weights. Select at least three independent specimen groups, review every selected field, and keep related fields or crops in the same group. Configurable, deterministic train/validation/test splits keep groups separate and reject duplicate source files. Validation selects the checkpoint; test images are reserved for the final report.

Only a real checkpoint with a verified evaluation report can be saved to Models. Reports include AP at IoU 0.5, precision, recall, F1 and mean diameter error in source pixels. Training uses full precision and requires at least six epochs. Cellpose 4/SAM, StarDist and Omnipose fine-tuning are outside this workflow. Dataset quality and independent biological validation remain the user's responsibility; software execution does not establish model accuracy.

Validation and scope

The release candidate passed 96 native unit/performance tests and 23 Python tests. A separate local CPU smoke run exercised six actual Cellpose 3 training epochs, checkpoint reload and held-out evaluation using generated fixtures and randomly initialized weights. That smoke run verifies execution, not biological accuracy.

This release updates the macOS app; it does not change Windows or web capability boundaries.

Download

Download CellCounter-v1.0.11.zip and its .sha256 checksum from the v1.0.11 release. Unzip and move CellCounting.app to Applications.

ZIP SHA-256: 289a9efe2ef748fe51dffcea01569b29ae0dc3d673345ac6d3791d164226ae84.