Update documentation

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 | Image |
-| — |
-| <img alt="Mammary Gland Cells" src="_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 <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>VollSeg specializes with its seed pooling approach to segment irregular shapes of</p>
+<p><img alt="Mammary Gland Cells" src="_images/Seg_pipe-git.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>
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_sources/MAMMARYGLAND.md

@@ -1,5 +1,7 @@
 # Segmentation of mammary gland cells
 
-VollSeg specializes with its seed pooling approach to segment irregular shapes of | Image |
-| --- |
-| ![Mammary Gland Cells](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.
+VollSeg specializes with its seed pooling approach to segment irregular shapes of 
+
+![Mammary Gland Cells](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.