|
11 | 11 | "2D", |
12 | 12 | "3D" |
13 | 13 | ], |
14 | | - "docs_info": "### Purpose\n- Converts 2D and 3D images from CellVoyager CV7000/8000 systems into OME-Zarr format, creating OME-Zarr HCS plates and combining all fields of view in a well into a single image.\n- Saves Fractal region-of-interest (ROI) tables for both individual fields of view and the entire well.\n- Handles overlapping fields of view by adjusting their positions to be non-overlapping while retaining the original position data as additional columns in the ROI tables.\n- Supports processing multiple plates in a single task.\n\n### Limitations\n- Currently, this task does not support time-resolved data and ignores the time fields in CellVoyager metadata.\n", |
| 14 | + "docs_info": "### Purpose\n- Converts **2D and 3D images from CellVoyager CV7000/8000** systems into OME-Zarr format, creating OME-Zarr HCS plates and combining all fields of view in a well into a single image.\n- Saves Fractal **region-of-interest (ROI) tables** for both individual fields of view and the entire well.\n- Handles overlapping fields of view by adjusting their positions to be non-overlapping while retaining the original position data as additional columns in the ROI tables.\n- Supports processing multiple plates in a single task.\n\n### Limitations\n- Currently, this task does not support time-resolved data and ignores the time fields in CellVoyager metadata.\n", |
15 | 15 | "executable_non_parallel": "tasks/cellvoyager_to_ome_zarr_init.py", |
16 | 16 | "executable_parallel": "tasks/cellvoyager_to_ome_zarr_compute.py", |
17 | 17 | "meta_non_parallel": { |
|
281 | 281 | "2D", |
282 | 282 | "3D" |
283 | 283 | ], |
284 | | - "docs_info": "### Purpose\n- Converts multiplexed 2D and 3D images from CellVoyager CV7000/8000 systems into OME-Zarr format, storing each acquisition as a separate OME-Zarr image in the same OME-Zarr plate.\n- Creates OME-Zarr HCS plates, combining all fields of view for each acquisition in a well into a single image.\n- Saves Fractal region-of-interest (ROI) tables for both individual fields of view and the entire well.\n- Handles overlapping fields of view by adjusting their positions to be non-overlapping, while preserving the original position data as additional columns in the ROI tables.\n\n### Limitations\n- This task currently does not support time-resolved data and ignores the time fields in CellVoyager metadata.\n", |
| 284 | + "docs_info": "### Purpose\n- Converts **multiplexed 2D and 3D images from CellVoyager CV7000/8000** systems into OME-Zarr format, storing each acquisition as a separate OME-Zarr image in the same OME-Zarr plate.\n- Creates **OME-Zarr HCS plates**, combining all fields of view for each acquisition in a well into a single image.\n- Saves Fractal **region-of-interest (ROI) tables** for both individual fields of view and the entire well.\n- Handles overlapping fields of view by adjusting their positions to be non-overlapping, while preserving the original position data as additional columns in the ROI tables.\n\n### Limitations\n- This task currently does not support time-resolved data and ignores the time fields in CellVoyager metadata.\n", |
285 | 285 | "executable_non_parallel": "tasks/cellvoyager_to_ome_zarr_init_multiplex.py", |
286 | 286 | "executable_parallel": "tasks/cellvoyager_to_ome_zarr_compute.py", |
287 | 287 | "meta_non_parallel": { |
|
573 | 573 | "Preprocessing", |
574 | 574 | "3D" |
575 | 575 | ], |
576 | | - "docs_info": "### Purpose\n- Performs Z-axis projection of intensity images using a specified projection method.\n- Generates a new OME-Zarr HCS plate to store the projected data.\n\n### Limitations\n- Supports projections only for OME-Zarr HCS plates; other collections of OME-Zarrs are not yet supported.\n- Currently limited to data in the CZYX format.\n", |
| 576 | + "docs_info": "### Purpose\n- Performs **Z-axis projection of intensity images** using a specified projection method.\n- **Generates a new OME-Zarr HCS plate** to store the projected data.\n\n### Limitations\n- Supports projections only for OME-Zarr HCS plates; other collections of OME-Zarrs are not yet supported.\n- Currently limited to data in the CZYX format.\n", |
577 | 577 | "executable_non_parallel": "tasks/copy_ome_zarr_hcs_plate.py", |
578 | 578 | "executable_parallel": "tasks/projection.py", |
579 | 579 | "meta_non_parallel": { |
|
700 | 700 | "2D", |
701 | 701 | "3D" |
702 | 702 | ], |
703 | | - "docs_info": "### Purpose\n- Corrects illumination in OME-Zarr images using pre-calculated flatfield profiles.\n- Optionally performs background subtraction.\n\n### Limitations\n- Requires pre-calculated flatfield profiles in TIFF format.\n- Supports only fixed-value background subtraction; background subtraction profiles are not supported.\n", |
| 703 | + "docs_info": "### Purpose\n- **Corrects illumination** in OME-Zarr images using **pre-calculated flatfield profiles**.\n- Optionally performs **background subtraction**.\n\n### Limitations\n- Requires pre-calculated flatfield profiles in TIFF format.\n- Supports only fixed-value background subtraction; background subtraction profiles are not supported.\n", |
704 | 704 | "executable_parallel": "tasks/illumination_correction.py", |
705 | 705 | "meta_parallel": { |
706 | 706 | "cpus_per_task": 1, |
|
772 | 772 | "2D", |
773 | 773 | "3D" |
774 | 774 | ], |
775 | | - "docs_info": "### Purpose\n- Segments images using Cellpose models.\n- Supports both built-in Cellpose models (shipped with Cellpose) and user-trained models.\n- Accepts dual image input for segmentation.\n- Can process arbitrary regions of interest (ROIs), including whole images, fields of view (FOVs), or masked outputs from prior segmentations, based on corresponding ROI tables.\n- Provides access to all advanced Cellpose parameters.\n- Allows custom rescaling options per channel, particularly useful for sparse images.\n\n### Limitations\n- Compatible only with Cellpose 2.x models; does not yet support 3.x models.\n", |
| 775 | + "docs_info": "### Purpose\n- **Segments images using Cellpose models**.\n- Supports both **built-in Cellpose models** (shipped with Cellpose) and **user-trained models**.\n- Accepts dual image input for segmentation.\n- Can process **arbitrary regions of interest (ROIs)**, including whole images, fields of view (FOVs), or masked outputs from prior segmentations, based on corresponding ROI tables.\n- Provides access to all advanced Cellpose parameters.\n- Allows custom rescaling options per channel, particularly useful for sparse images.\n\n### Limitations\n- Compatible only with Cellpose 2.x models; does not yet support 3.x models.\n", |
776 | 776 | "executable_parallel": "tasks/cellpose_segmentation.py", |
777 | 777 | "meta_parallel": { |
778 | 778 | "cpus_per_task": 4, |
|
1073 | 1073 | "2D", |
1074 | 1074 | "3D" |
1075 | 1075 | ], |
1076 | | - "docs_info": "### Purpose\n- Computes image-based registration transformations for acquisitions in HCS OME-Zarr datasets.\n- Processes images grouped by well, under the assumption that each well contains one image per acquisition.\n- Calculates transformations for specified regions of interest (ROIs) and stores the results in the corresponding ROI table.\n- Typically used as the first task in a workflow, followed by `Find Registration Consensus` and optionally `Apply Registration to Image`.\n\n### Limitations\n- Supports only HCS OME-Zarr datasets, leveraging their acquisition metadata and well-based image grouping.\n- Assumes each well contains a single image per acquisition.\n", |
| 1076 | + "docs_info": "### Purpose\n- **Computes image-based registration** transformations for acquisitions in **HCS** OME-Zarr datasets.\n- Processes images grouped by well, under the assumption that each well contains one image per acquisition.\n- Calculates transformations for **specified regions of interest (ROIs)** and stores the results in the corresponding ROI table.\n- Typically used as the first task in a workflow, followed by `Find Registration Consensus` and optionally `Apply Registration to Image`.\n\n### Limitations\n- Supports only HCS OME-Zarr datasets, leveraging their acquisition metadata and well-based image grouping.\n- Assumes each well contains a single image per acquisition.\n", |
1077 | 1077 | "executable_non_parallel": "tasks/image_based_registration_hcs_init.py", |
1078 | 1078 | "executable_parallel": "tasks/calculate_registration_image_based.py", |
1079 | 1079 | "meta_non_parallel": { |
|
1211 | 1211 | "2D", |
1212 | 1212 | "3D" |
1213 | 1213 | ], |
1214 | | - "docs_info": "### Purpose\n- Determines the consensus alignment region across all selected acquisitions within each well of an HCS OME-Zarr dataset.\n- Generates a new ROI table for each image, defining consensus regions that are aligned across all acquisitions.\n- Typically used as the second task in a workflow, following `Calculate Registration (image-based)` and optionally preceding `Apply Registration to Image`.\n\n### Limitations\n- Supports only HCS OME-Zarr datasets, leveraging their acquisition metadata and well-based image grouping.\n", |
| 1214 | + "docs_info": "### Purpose\n- Determines the **consensus alignment** region across all selected acquisitions **within each well of an HCS OME-Zarr dataset**.\n- Generates a new ROI table for each image, defining consensus regions that are aligned across all acquisitions.\n- Typically used as the second task in a workflow, following `Calculate Registration (image-based)` and optionally preceding `Apply Registration to Image`.\n\n### Limitations\n- Supports only HCS OME-Zarr datasets, leveraging their acquisition metadata and well-based image grouping.\n", |
1215 | 1215 | "executable_non_parallel": "tasks/init_group_by_well_for_multiplexing.py", |
1216 | 1216 | "executable_parallel": "tasks/find_registration_consensus.py", |
1217 | 1217 | "meta_non_parallel": { |
|
1320 | 1320 | "2D", |
1321 | 1321 | "3D" |
1322 | 1322 | ], |
1323 | | - "docs_info": "### Purpose\n- Applies pre-calculated registration transformations to images in an HCS OME-Zarr dataset, aligning all acquisitions to a specified reference acquisition.\n- Masks regions not included in the registered ROI table and aligns both intensity and label images.\n- Replaces the non-aligned image with the newly aligned image in the dataset if `overwrite input` is selected.\n- Typically used as the third task in a workflow, following `Calculate Registration (image-based)` and `Find Registration Consensus`.\n\n### Limitations\n- If `overwrite input` is selected, the non-aligned image is permanently deleted, which may impact workflows requiring access to the original images.\n", |
| 1323 | + "docs_info": "### Purpose\n- **Applies pre-calculated registration** transformations to images in an **HCS** OME-Zarr dataset, aligning all acquisitions to a specified reference acquisition.\n- **Masks regions not included** in the registered ROI table and aligns both intensity and label images.\n- Replaces the non-aligned image with the newly aligned image in the dataset if `overwrite input` is selected.\n- Typically used as the third task in a workflow, following `Calculate Registration (image-based)` and `Find Registration Consensus`.\n\n### Limitations\n- If `overwrite input` is selected, the non-aligned image is permanently deleted, which may impact workflows requiring access to the original images.\n", |
1324 | 1324 | "executable_parallel": "tasks/apply_registration_to_image.py", |
1325 | 1325 | "meta_parallel": { |
1326 | 1326 | "cpus_per_task": 1, |
|
1367 | 1367 | "2D", |
1368 | 1368 | "3D" |
1369 | 1369 | ], |
1370 | | - "docs_info": "### Purpose\n- Imports a single OME-Zarr dataset into the Fractal framework for further processing.\n- Supports importing either a full OME-Zarr HCS plate or an individual OME-Zarr image.\n- Ensures the OME-Zarr dataset is located in the `zarr_dir` specified by the dataset.\n- Generates the necessary image list metadata required for processing the OME-Zarr with Fractal.\n- Optionally adds new ROI tables to the existing OME-Zarr, enabling compatibility with many other tasks.\n\n### Limitations\n- Supports only OME-Zarr datasets already present in the `zarr_dir` of the corresponding dataset.\n- Assumes the input OME-Zarr is correctly structured and formatted for compatibility with the Fractal framework.\n", |
| 1370 | + "docs_info": "### Purpose\n- Imports a **single OME-Zarr dataset** into the Fractal framework for further processing.\n- Supports importing either a **full OME-Zarr HCS plate** or an **individual OME-Zarr image**.\n- Ensures the OME-Zarr dataset is located in the `zarr_dir` specified by the dataset.\n- Generates the necessary **image list metadata** required for processing the OME-Zarr with Fractal.\n- Optionally **adds new ROI tables** to the existing OME-Zarr, enabling compatibility with many other tasks.\n\n### Limitations\n- Supports only OME-Zarr datasets already present in the `zarr_dir` of the corresponding dataset.\n- Assumes the input OME-Zarr is correctly structured and formatted for compatibility with the Fractal framework.\n", |
1371 | 1371 | "executable_non_parallel": "tasks/import_ome_zarr.py", |
1372 | 1372 | "args_schema_non_parallel": { |
1373 | 1373 | "additionalProperties": false, |
|
1444 | 1444 | "2D", |
1445 | 1445 | "3D" |
1446 | 1446 | ], |
1447 | | - "docs_info": "### Purpose\n- Executes a Napari workflow on the regions of interest (ROIs) within a single OME-NGFF image.\n- Processes specified images and labels as inputs to the workflow, producing outputs such as new labels and data tables.\n- Offers flexibility in defining input and output specifications to customize the workflow for specific datasets and analysis needs.\n\n### Limitations\n- Currently supports only Napari workflows that utilize functions from the `napari-segment-blobs-and-things-with-membranes` module. Other Napari-compatible modules are not supported.\n\n### Input Specifications\nNapari workflows require explicit definitions of input and output data.\nExample of valid `input_specs`:\n```json\n{\n \"in_1\": {\"type\": \"image\", \"channel\": {\"wavelength_id\": \"A01_C02\"}},\n \"in_2\": {\"type\": \"image\", \"channel\": {\"label\": \"DAPI\"}},\n \"in_3\": {\"type\": \"label\", \"label_name\": \"label_DAPI\"}\n}\n```\n\nExample of valid `output_specs`:\n```json\n{\n \"out_1\": {\"type\": \"label\", \"label_name\": \"label_DAPI_new\"},\n \"out_2\": {\"type\": \"dataframe\", \"table_name\": \"measurements\"},\n}\n```\n", |
| 1447 | + "docs_info": "### Purpose\n- Executes a **napari workflow** on the regions of interest (ROIs) within a single OME-NGFF image.\n- Processes specified images and labels as inputs to the workflow, producing outputs such as new labels and data tables.\n- Offers **flexibility in defining input and output** specifications to customize the workflow for specific datasets and analysis needs.\n\n### Limitations\n- Currently supports only Napari workflows that utilize functions from the `napari-segment-blobs-and-things-with-membranes` module. Other Napari-compatible modules are not supported.\n\n### Input Specifications\nNapari workflows require explicit definitions of input and output data.\nExample of valid `input_specs`:\n```json\n{\n \"in_1\": {\"type\": \"image\", \"channel\": {\"wavelength_id\": \"A01_C02\"}},\n \"in_2\": {\"type\": \"image\", \"channel\": {\"label\": \"DAPI\"}},\n \"in_3\": {\"type\": \"label\", \"label_name\": \"label_DAPI\"}\n}\n```\n\nExample of valid `output_specs`:\n```json\n{\n \"out_1\": {\"type\": \"label\", \"label_name\": \"label_DAPI_new\"},\n \"out_2\": {\"type\": \"dataframe\", \"table_name\": \"measurements\"},\n}\n```\n", |
1448 | 1448 | "executable_parallel": "tasks/napari_workflows_wrapper.py", |
1449 | 1449 | "meta_parallel": { |
1450 | 1450 | "cpus_per_task": 8, |
|
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