2D Inference (Parameter Testing)

Parameters

image layer: The napari image layer on which to run model inference.

Model: Model to use for inference. Default options are MitoNet_v1 and MitoNet_v1_mini.

Image Downsampling: Downsampling factor to apply to the input image before running model inference. The returned segmentation will be interpolated to the original binary segmentation.

Center Confidence Thr: The minimum intensity of a peak in the centers heatmap for it to be considered a true object center.

Centers Min Distance: The minimum distance allowed between centers in pixels.

Fine boundaries: Whether to run Panoptic DeepLab postprocessing at 0.25x the input image resolution. Can correct some segmentation errors at the cost of 4x more GPU/CPU memory during postprocessing.

Semantic Only: Whether to skip panoptic postprocessing and return only a semantic segmentation.

Max objects per class: The maximum number of objects that are allowed for any one of the classes being segmented by the model.

Tile size: Size of image tiles in pixels. When set to 0, inference will be run on the entire image in a single pass. For non-zero tile sizes, the image will be partitioned into patches, model inference will be run independently on each, and the results are stitched together. By default, all tiles have a minimum overlap of 128 pixels with their neighbors.

Batch Mode: If checked, the selected model will be run independently on each xy slice in a stack of images. This can be used, for example, to run inference on all images in a folder by loading them with the “Open Folder…” option.

Use GPU: Whether to use system GPU for running inference. If no GPU is detected on the workstation, then this parameter is ignored.

Use quantized model: Whether to use a quantized version of the segmentation model. The quantized model only runs on CPU but uses ~4x less memory and runs 20-50% faster (depending on the model architecture). Results may be 1-2% worse than using the non-quantized version. Quantized models do not work on all operating systems and hardware configurations. Currently, they are not supported by Apple Silicon.

Confine to viewport: When checked, model inference will only be performed on the area of the image currently visible in the Napari viewing window. This makes it possible to navigate large images and selectively test model performance within a 2D ROI. This option will return an error if run on xz slices of a 3D image.

Output to layer: If checked, the output of the model will be inserted into the given output layer (next argument). This argument is incompatible with Batch mode and will raise an error if both are checked.

output layer: If Output to layer is checked, the output of the model will be inserted into this layer, otherwise a new layer will be created. The output layer must be the same shape as the image layer.

See Inference Best Practices for more usage notes.

Output

Returns a 2D labels layer in the napari viewer or inserts a predicted segmentation into a selected labels layer.

Demo

Check out the step-by-step tutorial here