Version bump + Wnet3d assumptions docs

Author: C-Achard

README.md

@@ -1,4 +1,4 @@
-# CellSeg3D: self-supervised (and supervised) 3D cell segmentation, primarily for mesoSPIM data! 
+# CellSeg3D: self-supervised (and supervised) 3D cell segmentation, primarily for mesoSPIM data!
 [![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-cellseg3d)](https://www.napari-hub.org/plugins/napari-cellseg3d)
 [![PyPI](https://img.shields.io/pypi/v/napari-cellseg3d.svg?color=green)](https://pypi.org/project/napari-cellseg3d)
 [![Downloads](https://static.pepy.tech/badge/napari-cellseg3d)](https://pepy.tech/project/napari-cellseg3d)
@@ -59,7 +59,7 @@ The strength of our approach is we can match supervised model performance with p
 
 ## News
 
-**New version: v0.2.0**
+**New version: v0.2.0post1**
 
 - Changed project name to "napari_cellseg3d" to avoid setuptools deprecation
 - Small API changes for training/inference from a script

docs/source/guides/inference_module_guide.rst

@@ -151,6 +151,15 @@ Unsupervised model - WNet3D
 | The `WNet3D model` is a fully self-supervised model used to segment images without any labels.
 | It functions similarly to the above models, with a few notable differences.
 
+WNet3D works best with :
+
+* **MesoSPIM** data (whole-brain samples of mice imaged by mesoSPIM microscopy) with nuclei staining.
+* Other microscopy data with :
+    * **Sufficient contrast** between objects and background.
+    * **Low to medium crowding** of objects. If all objects are adjacent to each other, instance segmentation methods provided here may not work well.
+
+Noise and object size are less critical, though objects still have to fit within the field of view of the model.
+
 .. _WNet3D model: https://arxiv.org/abs/1711.08506
 
 .. note::

docs/welcome.rst

@@ -11,7 +11,7 @@ Use CellSeg3D to:
 * Review labeled cell volumes from whole-brain samples of mice imaged by mesoSPIM microscopy [1]_
 * Train and use segmentation models from the MONAI project [2]_ or implement your own custom 3D segmentation models using PyTorch.
 
-No labeled data? Try our unsupervised model, based on the `WNet`_ model, to automate your data labelling.
+No labeled data? Try our unsupervised model **WNet3D**, based on the `WNet`_ model, to automate your data labelling.
 
 The models provided should be adaptable to other tasks related to detection of 3D objects,
 outside of whole-brain light-sheet microscopy.

napari_cellseg3d/__init__.py

@@ -1,2 +1,3 @@
 """napari-cellseg3d - napari plugin for cell segmentation."""
-__version__ = "0.2.0"
+
+__version__ = "0.2.0post1"

napari_cellseg3d/code_plugins/plugin_helper.py

@@ -1,4 +1,5 @@
 """Tiny plugin showing link to documentation and about page."""
+
 import pathlib
 from typing import TYPE_CHECKING
 
@@ -43,7 +44,7 @@ def __init__(self, viewer: "napari.viewer.Viewer"):
         self.logo_label.setToolTip("Open Github page")
 
         self.info_label = ui.make_label(
-            f"You are using napari-cellseg3d v.{'0.2.0'}\n\n"
+            f"You are using napari-cellseg3d v.{'0.2.0post1'}\n\n"
             f"Plugin for cell segmentation developed\n"
             f"by the Mathis Lab of Adaptive Motor Control\n\n"
             f"Code by :\nCyril Achard\nMaxime Vidal\nJessy Lauer\nMackenzie Mathis\n"

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = napari_cellseg3d
-version = 0.2.0
+version = 0.2.0post1
 
 [options]
 packages = find: