fixed docs + removed unused docs script

Author: C-Achard

docs/.nojekyll

@@ -1,4 +1,4 @@
-.. _model_framework.py_models_dict:
+.. _model_framework.py:
 
 
 model_framework.py

docs/buildsite.sh

@@ -15,7 +15,7 @@ binary_watershed
 
 clear_small_objects
 **************************************
-.. autofunction:: napari_cellseg_annotator.model_instance_seg::clear_small_objects
+.. autofunction:: napari_cellseg_3d.model_instance_seg::clear_small_objects
 
 to_instance
 **************************************

docs/res/code/model_framework.rst

@@ -8,7 +8,7 @@ Class : BasePluginSingleImage
 
 Methods
 **********************
-.. autoclass:: napari_cellseg_annotator.plugin_base::BasePluginSingleImage
+.. autoclass:: napari_cellseg_3d.plugin_base::BasePluginSingleImage
    :members:  __init__, remove_from_viewer, show_dialog_images, show_dialog_labels, update_default
    :noindex:
 
@@ -17,23 +17,25 @@ Methods
 Attributes
 *********************
 
-.. autoclass:: napari_cellseg_annotator.plugin_base::BasePluginSingleImage
+.. autoclass:: napari_cellseg_3d.plugin_base::BasePluginSingleImage
    :members:  _viewer, image_path, label_path, filetype, file_handling_box
 
 
+
+
 Class : BasePluginFolder
 -------------------------------------------------------
 
 
 Methods
 ***********************
-.. autoclass:: napari_cellseg_annotator.plugin_base::BasePluginFolder
+.. autoclass:: napari_cellseg_3d.plugin_base::BasePluginFolder
    :members:  __init__, remove_from_viewer,make_close_button,make_prev_button,make_next_button, load_dataset_paths,load_image_dataset,load_label_dataset,load_results_path, update_default,remove_docked_widgets
    :noindex:
 
 
 Attributes
 *********************
 
-.. autoclass:: napari_cellseg_annotator.plugin_base::BasePluginFolder
+.. autoclass:: napari_cellseg_3d.plugin_base::BasePluginFolder
    :members:  _viewer, images_filepaths, labels_filepaths,results_path, filetype_choice

docs/res/code/model_instance_seg.rst

@@ -1,7 +1,7 @@
 .. _convert_module_guide:
 
-Label conversion utility
-===========================
+Label conversion utility guide
+==================================
 
 This utility will let you convert labels to various different formats.
 You will have to specify the results directory for saving; afterwards you can run each action on a folder or on the currently selected layer.

docs/res/code/plugin_base.rst

@@ -1,6 +1,6 @@
 .. _cropping_module_guide:
 
-Cropping module guide
+Cropping utility guide
 =================================
 
 This module allows you to crop your volumes and labels dynamically,

docs/res/guides/convert_module_guide.rst

@@ -44,7 +44,9 @@ Interface and functionalities
 * Thresholding : You can perform thresholding to binarize your labels, all values beneath the confidence threshold will be set to 0 using this.
   If you wish to use instance segmentation it is recommended to use threshlding.
 
-
+* Instance segmentatin : You can convert the semantic segmentation into instance labels by using either the watershed or connected components method.
+  You can set the probability threshhold from which a pixel is considered as a valid instance, as well as the minimum size in pixels for objects. All smaller objects will be removed.
+  Instance labels will be saved (and shown if applicable) separately from other results.
 
 * Viewing results : You can also select whether you'd like to see the results in napari afterwards; by default the first image processed will be displayed,
   but you can choose to display up to ten at once. You can also request to see the originals.

docs/res/guides/cropping_module_guide.rst

@@ -14,13 +14,16 @@ any custom model written in Pytorch.
 
 From here you can access the guides on the several modules available for your tasks, such as :
 
-* Review : :ref:`loader_module_guide`
-* Inference: :ref:`inference_module_guide`
-* Training : :ref:`training_module_guide`
-* Cropping utility (3D) : :ref:`cropping_module_guide`
-* Convert labels : :ref:`convert_module_guide`
-* Compute scores : :ref:`metrics_module_guide`
-* Advanced : Defining custom models directly in the plugin (WIP) : :ref:`custom_model_guide`
+* Main modules :
+    * Review : :ref:`loader_module_guide`
+    * Inference: :ref:`inference_module_guide`
+    * Training : :ref:`training_module_guide`
+* Utilities :
+    * Cropping (3D) : :ref:`cropping_module_guide`
+    * Convert labels : :ref:`convert_module_guide`
+    * Compute scores : :ref:`metrics_module_guide`
+* Advanced :
+    * Defining custom models directly in the plugin (WIP) : :ref:`custom_model_guide`
 
 
 Requirements
@@ -63,10 +66,34 @@ Then go into Plugins > napari-cellseg-3d, and choose which tool to use.
 - **Review**: This module allows you to review your labels, from predictions or manual labeling, and correct them if needed. It then saves the status of each file in a csv, for easier monitoring.
 - **Infer**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells.
 - **Train**:  This module allows you to train segmentation algorithms from labeled volumes.
-- **Crop utility**: This module allows you to crop your volumes and labels dynamically, by selecting a fixed size volume and moving it around the image.
+- **Utilities**: This module allows you to use several utilities, e.g. to crop your volumes and labels, compute prediction scores or convert labels
 
 
+Credits & acknowledgments
+---------------------------------------------
+This plugin has been developed by Cyril Achard and Maxime Vidal for the `Mathis Laboratory of Adaptive Motor Control`_.
 
+The TRAILMAP models and original weights used here all originate from the `TRAILMAP project on GitHub`_
+
+    **Mapping Mesoscale Axonal Projections in the Mouse Brain Using A 3D Convolutional Network**
+    *Drew Friedmann, Albert Pun, Eliza L Adams, Jan H Lui, Justus M Kebschull, Sophie M Grutzner, Caitlin Castagnola, Marc Tessier-Lavigne, Liqun Luo*
+    bioRxiv 812644; doi: https://doi.org/10.1101/812644
+
+This plugin mainly uses the following libraries and software:
+
+* `Napari website`_
+
+* `Pytorch website`_
+
+* `MONAI project website`_ (various models used here are credited `on their website`_)
+
+
+.. _Mathis Laboratory of adaptive motor control: http://www.mackenziemathislab.org/
+.. _TRAILMAP project on GitHub: https://github.com/AlbertPun/TRAILMAP
+.. _Napari website: https://napari.org/
+.. _Pytorch website: https://pytorch.org/
+.. _MONAI project website: https://monai.io/
+.. _on their website: https://docs.monai.io/en/stable/networks.html#nets
 
 
 .. rubric:: References