Skip to content

Creating Training Targets

In this step, we will prepare the target data necessary for training our model and predicting the coordinates of proteins within a tomogram.

We will use Copick to manage the filesystem, extract runIDs, and create spherical targets corresponding to the locations of proteins. Key tasks include:

  • Generating Targets: For each tomogram, we extract particle coordinates and generate spherical targets based on these coordinates, and save the target data in OME Zarr format.

  • Target dimensions are determined with an associated tomogram, (specified by the --tomo-uri parameter, e.g. wbp@10.0).

  • Additional segmentations like organelles and membranes can be included as continuous targets with the --seg-target flag.

The segmentations are saved under the query specified by the --target-uri flag (name:user_id/session_id).

Method 1: Automated Query

The simplest approach is to let Octopi automatically find all pickable objects from a specific annotation source.

When to Use

  • ✅ Single annotation source: All picks come from one user/session
  • ✅ Quick setup: Minimal parameter specification

Example Command

Bash
octopi create-targets \
    --config config.json \
    --picks-user-id data-portal --picks-session-id 0 \
    --seg-target membrane \
    --tomo-uri wbp@10.0 \
    --target-uri targets:octopi/1

This command automatically finds all pickable objects associated with data-portal user and session 0, plus includes membrane segmentations.

Method 2: Manual Specification

For more control, manually specify which protein types and annotation sources to include. Users can define a subset of pickable objects (from the CoPick configuration file) by specifying the name, and optionally the userID and sessionID. This allows for creating customizable training targets from varying submission sources.

When to Use

  • ✅ Multiple annotation sources: Combining picks from different tools/users
  • ✅ Selective training: Only specific protein types needed
  • ✅ Quality control: Excluding low-quality annotations

Advanced Command

Bash
octopi create-targets \
    --config config.json \
    --target apoferritin --target beta-galactosidase:slabpick/1 \
    --target ribosome:pytom/0 --target virus-like-particle:pytom/0 \
    --seg-target membrane \
    --tomo-uri wbp@10.0 \
    --target-uri targets:octopi/1

Target Specification Formats

--target and --seg-target both accept the same query grammar:

Format Description Example
protein_name Use default user/session from config apoferritin
protein_name:user_id/session_id Specify source explicitly (preferred) ribosome:pytom/0
protein_name,user_id,session_id Legacy comma form (still supported) ribosome,pytom,0

Check Target Quality

To validate your training targets, refer to our interactive notebook: Inspect Segmentation Targets

This notebook shows how to load segmentation targets and overlay targets on tomograms.

Targets Example of training targets overlaid on tomogram slices. Different colors represent different protein classes.

Command Reference: octopi create-targets

Input Arguments

Parameter Description Example Required
--config Path to CoPick configuration file config.json ✅
--target Pickable object target(s): "name" or "name:user_id/session_id" ribosome:pytom/0 *
--picks-session-id Session ID for automated pick retrieval 0 *
--picks-user-id User ID for automated pick retrieval data-portal *
--seg-target Continuous segmentation target(s): "name" or "name:user_id/session_id" membrane ❌
--run-ids Specific run IDs to process run_001,run_002 ❌

*Either --target OR both --picks-session-id and --picks-user-id must be specified.

Processing Parameters

Parameter Description Default Example
--tomo-uri Tomogram URI in the form alg@voxel_size wbp@10.0 denoised@10.0
--radius-scale Scale factor for object radius 0.8 0.8 (80% of original radius)

Output Arguments

Parameter Description Default Example
--target-uri Output query as name, name:user_id, or name:user_id/session_id targets:octopi/1 my_targets:my_experiment/1