update mammary gland page

MAMMARYGLAND.md

@@ -1 +1,3 @@
-# Segmentation of mammary gland cells
+# Segmentation of mammary gland cells
+
+VollSeg specializes with its seed pooling approach to segment irregular shapes of [mammary gland cells](https://github.com/kapoorlab/VollSeg/blob/main/images/Seg_pipe-git.png) from human or mouse samples. In the orignal algorithm we use a CARE trained denoising model and eaither use the U-Net model for semantic segmentation or use the denoising model for the semantic segmentation depending on which model has a better prediction. For using combination of (U-Net, CARE and StarDist) model with U-Net as the model for semantic segmentation use this [script](scripts/mammary_gland_us.py) if you want to use the denoised image as the base image for creating the semantic segmentation map using Otsu threshold set the parameter ```dounet=False``` in that same script.

README.md

@@ -15,31 +15,14 @@ This project provides the [napari](https://napari.org/) plugin for [VollSeg](htt
 For detailed demo of the plugin see these [videos](https://www.youtube.com/watch?v=W_gKrLWKNpQ) and a short video about the [parameter selection](https://www.youtube.com/watch?v=7tQMn_u8_7s&t=1s) 
 
 
-
 ## Installation & Usage
 
-Install the plugin with `pip install vollseg-napari` or from within napari via `Plugins > Install/Uninstall Package(s)…`. If you want GPU-accelerated prediction, please read the more detailed [installation instructions](https://github.com/kapoorlab/vollseg-napari#gpu_installation) for VollSeg.
+Install the plugin with `pip install vollseg-napari` or from within napari via `Plugins > Install/Uninstall Package(s)…`. 
 
 You can activate the plugin in napari via `Plugins > VollSeg: VollSeg`. Example images for testing are provided via `File > Open Sample > VollSeg`.
 
 If you use this plugin for your research, please [cite us](http://conference.scipy.org/proceedings/scipy2021/varun_kapoor.html).
 
-## GPU_Installation
-
-This package is compatible with Python 3.6 - 3.9.
-
-1. Please first [install TensorFlow](https://www.tensorflow.org/install)
-(TensorFlow 2) by following the official instructions.
-For [GPU support](https://www.tensorflow.org/install/gpu), it is very
-important to install the specific versions of CUDA and cuDNN that are
-compatible with the respective version of TensorFlow. (If you need help and can use `conda`, take a look at [this](https://github.com/CSBDeep/CSBDeep/tree/master/extras#conda-environment).)
-
-2. *VollSeg* can then be installed with `pip`:
-
-    - If you installed TensorFlow 2 (version *2.x.x*):
-
-          pip install vollseg
-
 
 ## Examples
 

SPHEROIDS.md

@@ -11,7 +11,7 @@ The method used to segment the dual channels relies on the VollOne method inside
 
 ## Script
 
-The script used for this step is: [script](01_joint_membrane_nuclei_segmentation.py). 
+The script used for this step are: [script](scripts/spheroid_nuclei_membrane_segmentation.py) and [script](scripts/timelapse_spheroids_joint_segmentation.py). 
 
 
 

XENOPUS.md

@@ -13,7 +13,7 @@ For the membrane segmetation we have a trained cellpose model that segments the
 
 ## Script
 
-The scripts used for this step are: [membrane segmentation script](01_membrane_segmentation.py),  [nuclei segmentation script](01_nuclei_segmentation.py)
+The scripts used for this step are: [membrane segmentation script](scripts/cellpose_stardist_membrane.py),  [nuclei segmentation script](scripts/xenopus_nuclei.py)
 
 
 

scripts/mammary_gland_us.py

@@ -0,0 +1,60 @@
+import os
+import glob
+from tifffile import imread
+from vollseg import StarDist3D, UNET, CARE
+from vollseg.utils import VollSeg
+from pathlib import Path 
+def main():
+
+    image_dir = '/path/toimagedir'
+    model_dir = '/path/tomodeldir/'
+    save_dir = os.path.join(image_dir, 'VollSeg')
+    Path(save_dir).mkdir(exist_ok=True)
+
+    unet_model_name = 'unet_nuclei_model_name'
+    star_model_name = 'star_nuclei_model_name'
+    noise_model_name = 'noise_nuclei_model_name'
+
+    unet_model = UNET(config = None, name = unet_model_name, basedir = model_dir)
+    star_model = StarDist3D(config = None, name = star_model_name, basedir = model_dir)
+    noise_model = CARE(config = None, name = noise_model_name, basedir = model_dir)
+    Raw_path = os.path.join(image_dir, '.tif')
+    filesRaw = glob.glob(Raw_path)
+    filesRaw.sort
+    min_size = 10
+    min_size_mask = 10
+    max_size = 10000
+    n_tiles = (1,1,1)
+    dounet = True
+    seedpool = True
+    slice_merge = False 
+    UseProbability = True
+    donormalize = True
+    axes = 'ZYX'
+    ExpandLabels = False
+    for fname in filesRaw:
+
+                    image = imread(fname)
+                    Name = os.path.basename(os.path.splitext(fname)[0])
+                    VollSeg( image, 
+                            unet_model = unet_model, 
+                            star_model = star_model, 
+                            noise_model = noise_model,
+                            seedpool = seedpool, 
+                            axes = axes, 
+                            min_size = min_size,  
+                            min_size_mask = min_size_mask,
+                            max_size = max_size,
+                            donormalize=donormalize,
+                            n_tiles = n_tiles,
+                            ExpandLabels = ExpandLabels,
+                            slice_merge = slice_merge, 
+                            UseProbability = UseProbability, 
+                            save_dir = save_dir, 
+                            Name = Name,
+                            dounet = dounet) 
+
+
+
+if __name__ == '__main__':
+    main()