Merge pull request #81 from kbestak/stardist

Stardist

Author: kbestak

CITATIONS.md

@@ -35,6 +35,10 @@
 
 > Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.
 
+- [Stardist](https://github.com/stardist/stardist)
+
+  > Schmidt, U., Weigert, M., Broaddus, C., Myers, G. (2018). Cell Detection with Star-Convex Polygons. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science(), vol 11071. Springer, Cham. https://doi.org/10.1007/978-3-030-00934-2_30
+
 ## Software packaging/containerisation tools
 
 - [Anaconda](https://anaconda.com)

README.md

@@ -32,7 +32,7 @@ Image preprocessing
 
 Cell segmentation
 
-- Apply cell segmentation based on provided images, available options are: - [`Cellpose`](https://www.cellpose.org/) - [`Mesmer`](https://deepcell.readthedocs.io/en/master/API/deepcell.applications.html#mesmer) - [`ilastik`](https://www.ilastik.org/)
+- Apply cell segmentation based on provided images, available options are: - [`Cellpose`](https://www.cellpose.org/) - [`Mesmer`](https://deepcell.readthedocs.io/en/master/API/deepcell.applications.html#mesmer) - [`ilastik`](https://www.ilastik.org/) - [`Stardist`](https://github.com/stardist/stardist)
 - Filter cells based on cell size to remove artifacts
 
 Spot processing

conf/modules.config

@@ -173,6 +173,21 @@ process {
         ].join(" ").trim()
     }
 
+
+    withName: STARDIST {
+        ext.when  = { params.segmentation_method.split(',').contains('stardist') }
+        ext.args  = [ "",
+            params.stardist_model                                    ? "--model ${params.stardist_model}"                                    : "",
+            (params.stardist_n_tiles_x && params.stardist_n_tiles_y) ? "--n_tiles ${params.stardist_n_tiles_x} ${params.stardist_n_tiles_y}" : "",
+            ].join(" ").trim()
+        ext.prefix = { "${meta.id}_stardist_mask" }
+        publishDir = [
+            path: "${params.outdir}/segmentation/stardist",
+            pattern: "*.tif",
+            saveAs: { filename -> "${meta.id}_stardist_mask.tif" }
+        ]
+    }
+
     withName: "DEEPCELL_MESMER" {
         ext.when  = { params.segmentation_method.split(',').contains('mesmer') }
         ext.args  = [ "",

conf/test.config

@@ -28,7 +28,7 @@ params {
     mindagap_boxsize = 7
     mindagap_loopnum = 100
     clahe_pyramid_tile = 368
-    segmentation_method = "mesmer,cellpose"
+    segmentation_method = "mesmer,cellpose,stardist"
 
     // Only used if additionally params.create_training_subset is used
     crop_size_x = 30

conf/test_full.config

@@ -16,8 +16,10 @@ params {
 
     input = params.pipelines_testdata_base_path + 'molkart/test_data/samplesheets/samplesheet_full_test.csv'
 
-    segmentation_method = "mesmer,cellpose"
+    segmentation_method = "mesmer,cellpose,stardist"
     mindagap_boxsize = 3
     mindagap_loopnum = 40
     cellpose_pretrained_model = "nuclei"
+    stardist_n_tiles_x = 20
+    stardist_n_tiles_y = 20
 }

docs/output.md

@@ -73,6 +73,8 @@ Create stack is a local module used to merge images into a stack as preparation
     - `*_ilastik_mask.tif`: Segmentation masks created by ilastik's Boundary prediction with Multicut workflow.
   - `mesmer/`:
     - `*_mesmer_mask.tif`: Segmentation masks created by Mesmer.
+  - `stardist/`:
+    - `*_stardist_mask.tif`: Nuclear segmentation masks created by Stardist.
   - `filtered_masks/` - `*_method_filtered.tif`: Segmentation masks filtered based on provided area limits.
   </details>
 
@@ -82,6 +84,8 @@ Create stack is a local module used to merge images into a stack as preparation
 
 [Mesmer](https://deepcell.readthedocs.io/en/master/API/deepcell.applications.html#mesmer) is a segmentation tool that provides pretrained models for whole-cell and nuclear segmentation.
 
+[Stardist](https://github.com/stardist/stardist) is an object detection tool using star-convex shapes.
+
 ### Spot2cell
 
 <details markdown="1">

docs/usage.md

@@ -92,6 +92,9 @@ cellpose_pretrained_model: "cyto"
 cellpose_custom_model: null
 cellpose_flow_threshold: 0.4
 cellpose_edge_exclude: true
+stardist_model: "2D_versatile_fluo"
+stardist_n_tiles_x: 3
+stardist_n_tiles_y: 3
 mesmer_image_mpp: 0.138
 mesmer_compartment: "whole-cell"
 ilastik_pixel_project: null
@@ -121,6 +124,10 @@ To run the pipeline so that the training subset is created with default values,
 nextflow run nf-core/molkart --input ./samplesheet.csv --outdir ./results -profile docker --create_training_subset
 ```
 
+:::note
+Stardist segmentation currently only support nuclear segmentation and the additional marker will not be used.
+:::
+
 After training a Cellpose 2.0 model, or creating ilastik Pixel Classification and Multicut projects, make sure you match the parameters (e.g cell diameter, flow threshold) in the run to your training and continue the default pipeline run with:
 
 ```bash

modules.json

@@ -39,6 +39,11 @@
                         "branch": "master",
                         "git_sha": "81880787133db07d9b4c1febd152c090eb8325dc",
                         "installed_by": ["modules"]
+                    },
+                    "stardist": {
+                        "branch": "master",
+                        "git_sha": "666652151335353eef2fcd58880bcef5bc2928e1",
+                        "installed_by": ["modules"]
                     }
                 }
             },

modules/nf-core/stardist/environment.yml

@@ -16,6 +16,7 @@ params {
     segmentation_method        = 'mesmer'
     segmentation_min_area      = null
     segmentation_max_area      = null
+    // cellpose
     cellpose_save_flows        = false
     cellpose_diameter          = 30
     cellpose_chan              = 0
@@ -24,10 +25,16 @@ params {
     cellpose_custom_model      = null
     cellpose_flow_threshold    = 0.4
     cellpose_edge_exclude      = true
+    // mesmer
     mesmer_image_mpp           = 0.138
     mesmer_compartment         = 'whole-cell'
+    // ilastik
     ilastik_pixel_project      = null
     ilastik_multicut_project   = null
+    // stardist
+    stardist_model              = '2D_versatile_fluo'
+    stardist_n_tiles_x          = 3
+    stardist_n_tiles_y          = 3
 
     // Preprocessing command line flags
     skip_mindagap              = false