Merge branch 'master' into new_som

angelolab · Mar 2, 2021 · 4929fdb · 4929fdb
2 parents 1e3934b + 29d2c4a
commit 4929fdb
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Showing 2 changed files with 25 additions and 9 deletions.
diff --git a/ark/utils/deepcell_service_utils.py b/ark/utils/deepcell_service_utils.py
@@ -14,7 +14,7 @@
 
 
 def create_deepcell_output(deepcell_input_dir, deepcell_output_dir, fovs=None,
-                           suffix='_feature_0', host='https://deepcell.org', job_type='multiplex',
+                           suffix='_feature_0', host='https://deepcell.org', job_type='mesmer',
                            scale=1.0, timeout=3600, zip_size=100, parallel=False):
     """ Handles all of the necessary data manipulation for running deepcell tasks.
 
@@ -133,7 +133,7 @@ def _zip_run_extract(fov_group, group_index):
 
 
 def run_deepcell_direct(input_dir, output_dir, host='https://deepcell.org',
-                        job_type='multiplex', scale=1.0, timeout=3600):
+                        job_type='mesmer', scale=1.0, timeout=3600):
     """Uses direct calls to DeepCell API and saves output to output_dir.
 
         Args:
@@ -145,8 +145,7 @@ def run_deepcell_direct(input_dir, output_dir, host='https://deepcell.org',
                 Hostname and port for the kiosk-frontend API server.
                 Default: 'https://deepcell.org'
             job_type (str):
-                Name of job workflow (multiplex, segmentation, tracking).
-                Default: 'multiplex'
+                Name of job workflow (mesmer, segmentation, tracking).
             scale (float):
                 Value to rescale data by
                 Default: 1.0

diff --git a/templates/Segment_Image_Data.ipynb b/templates/Segment_Image_Data.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### This is a notebook to format your data for segmentation, run the images through the cloud instance of DeepCell, and then extract marker counts and morphological information from all the cells in your images"
+    "### This is a notebook to format your data for segmentation, run the images through the cloud instance of Mesmer, and then extract marker counts and morphological information from all the cells in your images"
    ]
   },
   {
@@ -25,6 +25,8 @@
     "import warnings\n",
     "import skimage.io as io\n",
     "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
     "\n",
     "from ark.utils import data_utils, deepcell_service_utils, io_utils, load_utils, io_utils, plot_utils, segmentation_utils\n",
     "from ark.segmentation import marker_quantification"
@@ -152,7 +154,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### load images into notebook, process, and save as deepcell compatable input"
+    "### load images into notebook, process, and save as Mesmer compatable input"
    ]
   },
   {
@@ -184,7 +186,7 @@
    },
    "outputs": [],
    "source": [
-    "# load channels to be included in deepcell data\n",
+    "# load channels to be included in Mesmer data\n",
     "channels = (nucs if nucs else []) + (mems if mems else [])\n",
     "\n",
     "# filter channels for None (just in case)\n",
@@ -276,11 +278,26 @@
    },
    "outputs": [],
    "source": [
-    "segmentation_labels = load_utils.load_imgs_from_dir(data_dir=deepcell_output_dir,\n",
+    "segmentation_labels_cell = load_utils.load_imgs_from_dir(data_dir=deepcell_output_dir,\n",
     "                                                    xr_dim_name='compartments',\n",
     "                                                    xr_channel_names=['whole_cell'],\n",
     "                                                    delimiter='_feature_0',\n",
-    "                                                    force_ints=True)"
+    "                                                    force_ints=True)\n",
+    "\n",
+    "segmentation_labels_nuc = load_utils.load_imgs_from_dir(data_dir=deepcell_output_dir,\n",
+    "                                                    xr_dim_name='compartments',\n",
+    "                                                    xr_channel_names=['nuclear'],\n",
+    "                                                    delimiter='_feature_1',\n",
+    "                                                    force_ints=True)\n",
+    "\n",
+    "segmentation_labels = xr.DataArray(np.concatenate((segmentation_labels_cell.values,\n",
+    "                                                  segmentation_labels_nuc.values),\n",
+    "                                                  axis=-1),\n",
+    "                                   coords=[segmentation_labels_cell.fovs, \n",
+    "                                           segmentation_labels_cell.rows,\n",
+    "                                           segmentation_labels_cell.cols,\n",
+    "                                           ['whole_cell', 'nuclear']],\n",
+    "                                   dims=segmentation_labels_cell.dims)"
    ]
   },
   {