build website

README.md

@@ -9,7 +9,7 @@
 [![Twitter Badge](https://badgen.net/badge/icon/twitter?icon=twitter&label)](https://twitter.com/entracod)
 
 
-VollSeg is more than just a single segmentation algorithm; it is a meticulously designed modular segmentation tool tailored to diverse model organisms and imaging methods. While a U-Net might suffice for certain image samples, others might benefit from utilizing StarDist, and some could require a blend of both, potentially coupled with denoising or region of interest models. The pivotal decision left to make is how to select the most appropriate VollSeg configuration for your dataset, a question we comprehensively address in our documentation.
+VollSeg is more than just a single segmentation algorithm; it is a meticulously designed modular segmentation tool tailored to diverse model organisms and imaging methods. While a U-Net might suffice for certain image samples, others might benefit from utilizing StarDist, and some could require a blend of both, potentially coupled with denoising or region of interest models. The pivotal decision left to make is how to select the most appropriate VollSeg configuration for your dataset, a question we comprehensively address in our [documentation website](https://kapoorlabs-caped.github.io/vollseg-napari/).
 
 This project provides the [napari](https://napari.org/) plugin for [VollSeg](https://github.com/kapoorlab/vollseg), a deep learning based 2D and 3D segmentation tool for irregular shaped cells. VollSeg has originally been developed (see [papers](http://conference.scipy.org/proceedings/scipy2021/varun_kapoor.html)) for the segmentation of densely packed membrane labelled cells in challenging images with low signal-to-noise ratios. The plugin allows to apply pretrained and custom trained models from within napari.
 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) 

_build/html/MAMMARYGLAND.html

@@ -358,9 +358,9 @@ <h1>Segmentation of mammary gland cells</h1>
 
   <section class="tex2jax_ignore mathjax_ignore" id="segmentation-of-mammary-gland-cells">
 <h1>Segmentation of mammary gland cells<a class="headerlink" href="#segmentation-of-mammary-gland-cells" title="Permalink to this headline">#</a></h1>
-<p>VollSeg specializes with its seed pooling approach to segment irregular shapes of</p>
+<p>VollSeg specializes with its seed pooling approach to segment irregular shapes of mammary gland cells</p>
 <p><img alt="Mammary Gland Cells" src="_images/Seg_compare-big.png" /></p>
-<p>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 <a class="reference download internal" download="" href="_downloads/55ced61326261fee22b55dba8817fd8e/mammary_gland_us.py"><span class="xref download myst">script</span></a> if you want to use the denoised image as the base image for creating the semantic segmentation map using Otsu threshold set the parameter <code class="docutils literal notranslate"><span class="pre">dounet=False</span></code> in that same script.</p>
+<p>from human or mouse samples. In the orignal algorithm we use a CARE trained denoising model and either use the U-Net model for semantic segmentation or use a denoising model for the semantic segmentation depending on which model has a better prediction. Denoising model is used to denoise the image first and the result is then passed to the segmentation models. For using combination of (U-Net, CARE and StarDist) model with U-Net as the model for semantic segmentation use this <a class="reference download internal" download="" href="_downloads/55ced61326261fee22b55dba8817fd8e/mammary_gland_us.py"><span class="xref download myst">script</span></a> if you want to use the denoised image as the base image for creating the semantic segmentation map using Otsu threshold set the parameter <code class="docutils literal notranslate"><span class="pre">dounet=False</span></code> in that same script.</p>
 </section>
 
     <script type="text/x-thebe-config">

_build/html/SPHEROIDS.html

@@ -381,7 +381,7 @@ <h2>VollOne Algorithm<a class="headerlink" href="#vollone-algorithm" title="Perm
 </section>
 <section id="script">
 <h2>Script<a class="headerlink" href="#script" title="Permalink to this headline">#</a></h2>
-<p>The script used for this step are: <a class="reference download internal" download="" href="_downloads/7ad3a4992554c10f7ec146ec06b4e82c/spheroid_nuclei_membrane_segmentation.py"><span class="xref download myst">script</span></a> and <a class="reference download internal" download="" href="_downloads/9db6ac1e5e9802c20682b7301efe0d04/timelapse_spheroids_joint_segmentation.py"><span class="xref download myst">script</span></a>.</p>
+<p>The script used for this step are: (for CZYX) <a class="reference download internal" download="" href="_downloads/7ad3a4992554c10f7ec146ec06b4e82c/spheroid_nuclei_membrane_segmentation.py"><span class="xref download myst">script</span></a> and (for CTZYX) <a class="reference download internal" download="" href="_downloads/9db6ac1e5e9802c20682b7301efe0d04/timelapse_spheroids_joint_segmentation.py"><span class="xref download myst">script</span></a>.</p>
 </section>
 </section>
 

_build/html/_sources/MAMMARYGLAND.md

@@ -1,7 +1,7 @@
 # Segmentation of mammary gland cells
 
-VollSeg specializes with its seed pooling approach to segment irregular shapes of 
+VollSeg specializes with its seed pooling approach to segment irregular shapes of mammary gland cells
 
 ![Mammary Gland Cells](images/Seg_compare-big.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.
+from human or mouse samples. In the orignal algorithm we use a CARE trained denoising model and either use the U-Net model for semantic segmentation or use a denoising model for the semantic segmentation depending on which model has a better prediction. Denoising model is used to denoise the image first and the result is then passed to the segmentation models. 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.

_build/html/_sources/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 are: [script](scripts/spheroid_nuclei_membrane_segmentation.py) and [script](scripts/timelapse_spheroids_joint_segmentation.py). 
+The script used for this step are: (for CZYX) [script](scripts/spheroid_nuclei_membrane_segmentation.py) and (for CTZYX) [script](scripts/timelapse_spheroids_joint_segmentation.py).