Merge pull request #49 from OmooLab/feature/dev

feat: improve eevee render

bioxelnodes/blender_manifest.toml

@@ -1,7 +1,7 @@
 schema_version = "1.0.0"
 
 id = "bioxelnodes"
-version = "1.0.2"
+version = "1.0.5"
 name = "Bioxel Nodes"
 tagline = "For scientific volumetric data visualization in Blender"
 maintainer = "Ma Nan <icrdr2010@outlook.com>"

bioxelnodes/constants.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 
-VERSIONS = [{"label": "Latest", "node_version": (1, 0, 2)},
+VERSIONS = [{"label": "Latest", "node_version": (1, 0, 5)},
             {"label": "v0.3.x", "node_version": (0, 3, 3)},
             {"label": "v0.2.x", "node_version": (0, 2, 9)}]
 
@@ -165,7 +165,13 @@ MENU_ITEMS = [
                 'icon': 'PROP_CON',
                 'name': 'ReCenter',
                 'description': ''
-            }
+            },
+            {
+                'label': 'Extract Transform',
+                'icon': 'EMPTY_AXIS',
+                'name': 'ExtractTransform',
+                'description': ''
+            },
         ]
     },
     {

bioxelnodes/menus.py

@@ -19,7 +19,7 @@ from .operators.container import (AddLocator, AddSlicer, ContainerProps,
                                   ExtractNodeMesh, ExtractNodeBboxWire, ExtractNodeShapeWire)
 
 from .operators.io import (ImportAsLabel, ImportAsScalar, ImportAsColor)
-from .operators.misc import (CleanTemp,
+from .operators.misc import (CleanTemp, Help,
                              ReLinkNodeLib, RemoveAllMissingLayers,
                              RenderSettingPreset, SaveAllLayersCache,
                              SaveNodeLib, SliceViewer)
@@ -57,6 +57,10 @@ class IncompatibleMenu(bpy.types.Menu):
         layout.separator()
         layout.menu(RenderSettingMenu.bl_idname)
 
+        layout.separator()
+        layout.operator(Help.bl_idname,
+                        icon=Help.bl_icon)
+
 
 class FetchLayerMenu(bpy.types.Menu):
     bl_idname = "BIOXELNODES_MT_ADD_LAYER"
@@ -210,6 +214,10 @@ class BioxelNodesTopbarMenu(bpy.types.Menu):
         layout.separator()
         layout.menu(RenderSettingMenu.bl_idname)
 
+        layout.separator()
+        layout.operator(Help.bl_idname,
+                        icon=Help.bl_icon)
+
 
 def TOPBAR(self, context):
     layout = self.layout

bioxelnodes/operators/io.py

@@ -96,8 +96,8 @@ class ImportAsLabel(bpy.types.Operator, ImportVolumetricData):
 
 class ImportAsColor(bpy.types.Operator, ImportVolumetricData):
     bl_idname = "bioxelnodes.import_as_color"
-    bl_label = "Import as Label"
-    bl_description = "Import Volumetric Data to Container as Label"
+    bl_label = "Import as Color"
+    bl_description = "Import Volumetric Data to Container as Color"
     bl_icon = "EVENT_C"
     read_as = "COLOR"
 
@@ -505,7 +505,7 @@ class ImportVolumetricDataDialog(bpy.types.Operator):
     label_count: bpy.props.IntProperty()  # type: ignore
 
     smooth: bpy.props.IntProperty(name="Smooth Size (Larger takes longer time)",
-                                  default=3)  # type: ignore
+                                  default=0)  # type: ignore
 
     read_as: bpy.props.EnumProperty(name="Read as",
                                     default="SCALAR",
@@ -624,7 +624,7 @@ class ImportVolumetricDataDialog(bpy.types.Operator):
                                                                   progress,
                                                                   progress_step)
                     label_data = data == np.full_like(data, i+1)
-                    label_data = label_data.astype(np.float32)
+                    # label_data = label_data.astype(np.float32)
                     try:
                         layer = Layer(data=label_data,
                                       name=name_i,
@@ -854,6 +854,7 @@ class ImportVolumetricDataDialog(bpy.types.Operator):
             bpy.ops.bioxelnodes.render_setting_preset('EXEC_DEFAULT',
                                                       preset="balance")
             bpy.context.scene.render.engine = 'CYCLES'
+            bpy.ops.bioxelnodes.slice_viewer('EXEC_DEFAULT')
 
         self.report({"INFO"}, "Successfully Imported")
         return {'FINISHED'}

bioxelnodes/operators/layer.py

@@ -370,8 +370,6 @@ class FillOperator(OutputLayerOperator):
     def invoke(self, context, event):
         layer_obj = get_selected_layer(context)
         if layer_obj:
-            scalar_min = get_layer_prop_value(layer_obj, "min")
-            self.fill_value = min(scalar_min, 0)
             name = get_layer_label(layer_obj)
             context.window_manager.invoke_props_dialog(self,
                                                        title=f"Fill {name}")

bioxelnodes/operators/misc.py

@@ -1,3 +1,4 @@
+import webbrowser
 import bpy
 from pathlib import Path
 import shutil
@@ -145,7 +146,7 @@ class RenderSettingPreset(bpy.types.Operator):
     def execute(self, context):
         if self.preset == "performance":
             # EEVEE
-            bpy.context.scene.eevee.use_taa_reprojection = False
+            # bpy.context.scene.eevee.use_taa_reprojection = False
             bpy.context.scene.eevee.volumetric_tile_size = '2'
             bpy.context.scene.eevee.volumetric_shadow_samples = 32
             bpy.context.scene.eevee.volumetric_samples = 64
@@ -161,7 +162,7 @@ class RenderSettingPreset(bpy.types.Operator):
 
         elif self.preset == "balance":
             # EEVEE
-            bpy.context.scene.eevee.use_taa_reprojection = False
+            # bpy.context.scene.eevee.use_taa_reprojection = False
             bpy.context.scene.eevee.volumetric_tile_size = '2'
             bpy.context.scene.eevee.volumetric_shadow_samples = 64
             bpy.context.scene.eevee.volumetric_samples = 128
@@ -177,7 +178,7 @@ class RenderSettingPreset(bpy.types.Operator):
 
         elif self.preset == "quality":
             # EEVEE
-            bpy.context.scene.eevee.use_taa_reprojection = False
+            # bpy.context.scene.eevee.use_taa_reprojection = False
             bpy.context.scene.eevee.volumetric_tile_size = '2'
             bpy.context.scene.eevee.volumetric_shadow_samples = 128
             bpy.context.scene.eevee.volumetric_samples = 256
@@ -201,7 +202,7 @@ class SliceViewer(bpy.types.Operator):
     bl_icon = "FILE_VOLUME"
 
     def execute(self, context):
-        bpy.context.scene.eevee.use_taa_reprojection = False
+        # bpy.context.scene.eevee.use_taa_reprojection = False
         bpy.context.scene.eevee.volumetric_tile_size = '2'
         bpy.context.scene.eevee.volumetric_shadow_samples = 128
         bpy.context.scene.eevee.volumetric_samples = 128
@@ -260,3 +261,16 @@ class RemoveAllMissingLayers(bpy.types.Operator, RemoveLayers):
                                               event,
                                               message=f"Are you sure to remove all **Missing** layers?")
         return {'RUNNING_MODAL'}
+
+
+class Help(bpy.types.Operator):
+    bl_idname = "bioxelnodes.help"
+    bl_label = "Need Help?"
+    bl_description = "Online Manual for Beginner"
+    bl_icon = "HELP"
+
+    def execute(self, context):
+        webbrowser.open(
+            'https://omoolab.github.io/BioxelNodes/latest/', new=2)
+
+        return {'FINISHED'}

docs/SARS-Cov-2.md

@@ -0,0 +1,99 @@
+# SARS-Cov-2
+
+![alt text](assets/SARS-Cov-2/image.png)
+
+![alt text](assets/SARS-Cov-2/image-1.png)
+
+In this tutorial, we'll make a structural visualzation of the SARS-Cov-2 Virus. Please see [step by step](step_by_step.md) to make sure you have a basic understanding of how to use the addon, and make sure the addon version is v1.0.2 or higher!
+
+Research data is from paper published in September 2020 in Cell Press ([https://www.cell.com/cell/fulltext/S0092-8674(20)31159-4](<https://www.cell.com/cell/fulltext/S0092-8674(20)31159-4>)) by Li Sai team at Tsinghua University. This research revealed the molecular assembly of the authentic SARS-CoV-2 virus using cryoelectron tomography (cryo-ET) and subtomogram averaging (STA). Here is an official video of this study
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/JSKl5VY4k5w?si=t4LhrCRebxrX_Saf&amp;controls=0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+"It remains enigmatic how coronaviruses pack the ∼30-kb RNA into the ∼80-nm-diameter viral lumen. Are the RNPs ordered relative to each other to avoid RNA entangling, knotting, or even damage or are they involved in virus assembly?" Three assembly classes were proposed in the paper, we will make a visualzation of the "eggs-in-a-nest"-shaped RNPs assembly, based on real cryo-ET data.
+
+## Download and import data
+
+The data of this research is hosted on EMDB ([https://www.ebi.ac.uk/emdb](https://www.ebi.ac.uk/emdb)), and the website of SARS-Cov-2 virus is [here](https://www.ebi.ac.uk/emdb/EMD-30430). Open the page, find "Download" button and select the first selection "3D Volume (.map.gz)".
+
+In case you can't download data, you can download here.
+
+[SARS-Cov-2.map](https://drive.google.com/file/d/1LMybsmTVbwQ38_eqAx6hbZTc5p2fdcjK/view?usp=sharing)
+
+After downloading, put it into any directory and import the data. In the import options, it is recommended to adjust the Bioxel Size to 5, in order to reduce the shape of the data to increase the speed of calculation and rendering. If you are confident in the performance of your hardware, you can leave the original 2.72 unchanged. After adjusting the Bioxel Size, the shape of the converted data will be calculated below based on the current bioxel size. The amount of data will increase exponentially with larger shape, and may result in OOM (out of memory).
+
+## Cutout the Virus
+
+After importing, you can get the virus by creating and connecting nodes and setting parameters as shown below
+
+![alt text](assets/SARS-Cov-2/image-2.png)
+
+Place the light and the camera in the way as below. The backlight is a area light, color white, intensity 500W, spread 90°, and inside the virus, place a point light, color `FFD08D`, intensity 5W. Camera focal length is 200 mm. the background color is pure black. The Look of the color management is High Contrast.
+
+![alt text](assets/SARS-Cov-2/image-3.png)
+
+Continue editing the node, followed by "Set Properies" and "Set Color by Ramp 4" node, the parameters of these two nodes are set as shown in the figure, where the color part, from top to bottom, is set to `E1D0FC` (1.0), `FFE42D` (0.5), `3793FF` (0.5), `FFFF8EC` (0.1) (alpha value in parentheses). Color alpha also affects density.
+
+![alt text](assets/SARS-Cov-2/image-4.png)
+
+If you feel that rendering is too slow, see [here](improve_performance.md) for suggestions
+
+## Color the RNPs alone
+
+The current rendering is pretty good, but considering that my goal is to make it clear how the RNPs are arranged within the virus, the RNPs should be colored differently than the others. So how to color the RNPs alone?
+
+First of all, we have to separate the RNPs. The value of RNPs is between the membrane and S protein, so it is very difficult to separate. The good thing is that the virus is spherical, we can separate the RNPs part of the virus from the rest by using a sphere cutter. To do so, in the Geometry Nodes panel menu of the container, click **Bioxel Nodes > Add a Cutter > Sphere Cutter**, and adjust the scale and position of the newly created sphere object named "Sphere_Cutter" appropriately, so that it can just separate the internal RNPs and membrane of the virus.
+
+![alt text](assets/SARS-Cov-2/image-5.png)
+
+The process of changing the position of the "Sphere_Cutter" object can be very laggy, then you can turn off "With Surface" and do it in Slice Viewer mode. Once you are happy with it, you can restore the settings.
+
+Then we turn on Invert in the "Sphere Cutter" node and you can see the opposite result. This way we've managed to separate the two, next let's color them separately and finally merge them with Join Component. Create and join the nodes as shown below, where the second Cutter node is copied and the color value in Set Color is `FFDDFE` (0.5). If all is well, the rendering should look like below.
+
+![alt text](assets/SARS-Cov-2/image-6.png)
+
+At this point, the rendering of a SARS-Cov-2 virus is complete!
+
+## Render RNPs and Membranes
+
+Realistic rendering is great, but Non-photorealistic rendering (NPR) is better for presenting structural information. So I will render ultrastructure of the RNPs assembly with toon shader.
+
+![alt text](assets/SARS-Cov-2/image-7.png)
+
+The silhouettes of RNPs uses Blender's Line Art feature, but Line Art must be based on a mesh. Select the nodes that are extruding the RNPs (i.e., the "Cut" nodes that are in charge of cutting out the RNPs), and right-click, click **Bioxel Nodes > Extract Mesh**. This gives you a new mesh object of the RNPs.
+
+Extract the membrane mesh in the same way.
+
+The membrane has a much higher value than the rest of the virus, so let's create a new "Cutout" node, also after the ReCenter, but with the threshold adjusted to 0.13. After the Cutout, connect to the "To Surface" node and parameterize it as shown in the figure, noting that the Remove Island Threshold is set to 1000 so that any fragments smaller than 1000 pts be eliminated. Then select the "To Surface" node, and perform the operation of extracting the mesh.
+
+![alt text](assets/SARS-Cov-2/image-8.png)
+
+The mesh of the membrane needs to be cut in half, this can be done with the Box Trim brush in sculpt mode or with the Boolean modifier. the mesh of the RNPs may also need some repair work, I cleaned up some of the broken surfaces of the RNPs. all the meshes are now ready to be used.
+
+![alt text](assets/SARS-Cov-2/image-9.png)
+
+For Line Art, please see the video tutorial from Blender Secrets.
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/aIWdBq7-ias?si=CrcStx5VVJBwDpzu&amp;controls=0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+Finally, an infographic on the ultrastructure of RNPs is done!
+
+![alt text](assets/SARS-Cov-2/image-1.png)
+
+If you have trouble following the docs, you can download the project files.
+
+[SARS-Cov-2.zip](https://drive.google.com/file/d/15GpIoIjVAE-Jr98zWo7oupuk1KfRVPmk/view?usp=sharing)
+
+## Homework
+
+Other data relevant to the study are provided in the paper, you could try visualizing them.
+
+![alt text](assets/SARS-Cov-2/image-10.png)
+
+Go to the official EMDB website https://www.ebi.ac.uk/emdb/ and enter the EMD number in the search box to get them.
+
+The Electron Microscopy Data Bank (EMDB) is a public repository for cryogenic-sample Electron Microscopy (cryoEM) volumes and representative tomograms of macromolecular complexes and subcellular structures. If you want to get data on other virus, just enter their name in the search box. For example, hepatitis B virus, enter it into the search box, then select Virus in the Sample Type on the left side, and download the data you need (it is better to find the corresponding paper to clarify the data information).
+
+**Try to visualize hepatitis B virus.**
+
+![alt text](assets/SARS-Cov-2/image-11.png)

docs/advanced.md

@@ -1,3 +0,0 @@
-# Advanced
-
-WIP

docs/advanced_usage.md

@@ -0,0 +1,118 @@
+# Advanced Usage
+
+## Concepts
+
+In the previous section, I mentioned some key concepts, I'll describe them in detail so that you can better understand the following operations.
+
+### Container
+
+First is the **Container**, it is the easiest to describe, you can think of it as a scene, or a solver container. it is the stage that carries all stuffs.
+
+### Layer
+
+Second is the **Layer**, which represents different fields of data under the same location in container of the same subject. Just as view layers in any map app, one as roads, one as satellite view, while in Bioxel, one as MRI, one as CT, one as electrical signals, etc. Some layers are for material, such as color, density, etc., we call them **Material Layer**. One container contains different layers of one subject each. Any external data imported into the container is converted into the layer.
+
+### Component
+
+Finally, the **Component**, which is a fragment of **Material Layer**. Multiple materials (Matters) may exist within the same area with different meanings of presentation, like objects in blender, each representing a part of the scene. Bioxel Nodes provides a series of nodes and tools that allow the user to create material layers (Components) based on the other layers (serve as source data), In the previous section, we used the CT data (layer) to make the material (matter) of skull, this process known as materialization.
+
+## Attache the Muscles to the Skull
+
+Multiple components can exist in one container, just as multiple objects can exist in one scene. They can be controlled individually or combined with each other to build complex scenes. Next, let's try cutouting muscle from the VHP anatomical images and attaching the muscles to the skull that created in the previous section. The anatomical images are very large, so I've cropped and scaled them down to 1/4 of their original size for ease of use. Download the file and unzip it into a new directory.
+
+[VHP_M_AnatomicalImages_Head.zip](https://drive.google.com/file/d/164AjQX0tmgUpWZlleJ5i1IKHco4ytDu2/view?usp=drive_link)
+
+You can also download the data directly from the official website, [https://data.lhncbc.nlm.nih.gov/public/Visible-Human/Male-Images/PNG_format/head/index.html](https://data.lhncbc.nlm.nih.gov/public/Visible-Human/Male-Images/PNG_format/head/index.html)
+
+First import the CT data as described in [step by step](step_by_step.md). Then import the anatomical images, but unlike before, this time from the Geometry Nodes menu of the existing container (not the top menu), click **Bioxel Nodes > Import Volumetric Data (Init) > as Color**, locate to the unzipped folder, and select any .png file (do not multi-select, and do not select the folder). This way the imported layer will be in the same container as the previous CT data. You can also import by dragging it into the container geometry node interface instead of the 3D Viewport interface. Also note that **as Color** (not as Scalar) is clicked because the anatomical images are RGB data.
+
+![alt text](assets/advanced_usage/image.png)
+
+Let's take more time in the import options this time 🫡, first let's talk about what the **Original Spacing** is in the options, you can simply think of it as the original "pixel size" of a image, since the volume is 3D, its pixels (i.e., voxels) are cuboids with a length, width, and height rather than 2D rectangles. And their length, width, and height size is the **Spacing**.
+
+Normally, Bioxel Nodes can read the spacing from the data header, and you don't need to fill in the value, but there is no record of that in any PNG image, so you need to input it manually. in the official description of the VHP, it is mentioned that each pixel of the anatomical images corresponds to the size of .33mm, and the thickness of each slice is 1mm, so the Original Spacing of the anatomical images should be set to `(0.33, 0.33, 1)`.
+
+However, the file I provided is scaled by 1/4 for each image, so the X and Y axes need to be multiplied by 4. So the final Original Spacing should be set to `(1.33, 1.33, 1)`, which should be understandable, right? (If you are importing an official file, then it should still be set to `(0.33, 0.33, 1)`)
+Layer Name should be set to "Anatomical Images", other options remain default, click Ok.
+
+![alt text](assets/advanced_usage/image-1.png)
+
+After importing, connect the "Cutout by Hue" node (Add > Bioxel Nodes > Component > Cutout by Hue) after the newly created "Fetch Layer" node, and turn on the Slice Viewer mode preview, as shown below.
+
+![alt text](assets/advanced_usage/image-2.png)
+
+In addition to spacing, the Dicom file also records the subject's position and orientation, but those information is apparently not recorded in PNG format. This results in a mismatch between the CT data and the anatomical images in position. We need to manually align this two layers: First, set the threshold of the "Cutout by Threshold" node to 0.2 so that the full head can be cutouted and displayed. Then add "ReCenter" node before "Cutout by Hue" node and "Cutout by Threshold" node each, then add Locater to the "anatomical images" workflow (see [step by step](step_by_step.md) for details), and finally add "Join Component" node (Add > Bioxel Nodes > Component > Join Component), and connect them as shown below, with the parameters set as shown.
+
+![alt text](assets/advanced_usage/image-3.png)
+
+The "Join Component" node allows you to merge two components together. Move and rotate the Locater object so that the two components are exactly the same in position. If you're having trouble matching them up, here's the exact transform values to fill in the Locater object properties.
+
+![alt text](assets/advanced_usage/image-4.png)
+
+Next let's cutout the muscles individually.
+
+First let's talk about the "Cutout by Hue" node. Literally, it cutout regions by hue of color. If you select this node and press "M" key to temporarily mute it, you'll see that the data becomes a cube with blue parts in Slice Viewer mode. the default value of Hue in the "Cutout by Hue" node is red, meaning that the node retains areas of the body that are close to red, and strip out the blue, which is on the opposite side of the red in the hue ring, leaving the human body parts.
+
+If you turn down the value of Sensitivity, you'll see that the visabile part is decreasing, leaving only the part more closer to red, and the muscle happens to be red, so we can easily cutout the muscle. The color of the specimen tissue is not as vibrant as it is in the live state, and we can fine-tune the color to restore the vibrancy by using the "Set Color by Color" node (Add > Bioxel Nodes > Property > Set Color by Color).
+
+To finally put it all together, connect and set the nodes as shown below, where the two colors in "Set Color by Ramp 2" node are `E7D2C5` and `FFEDEC`. If everything is working correctly, you'll get the rendering below.
+
+![alt text](assets/advanced_usage/image-5.png)
+
+You can also add "Transfrom" and "Cutter" nodes at the end and all components.
+
+![alt text](assets/advanced_usage/image-6.png)
+
+The multimodal data of VHP do not have any deformation or scaling differences, but only positional differences, so all data can be aligned easily. However, in most cases, the differences of different modalities are so great that they need to be registered first, which is not possible with Bioxel Nodes at the moment (but it is in plan). Nevertheless, you can build different components based on the same layer and combine them together. For example, based on the CT data, with the "Cutout by Range" node, you can eliminate the low value regions (fat), eliminate the high value regions (bone), and you can still get the muscle. And then attach it to the skull.
+
+![alt text](assets/advanced_usage/image-7.png)
+
+However, the CT values of brain and muscle are very close to each other, so it is extremely difficult to separate the brain from the muscle by threshold. So how should cutout the brain?
+
+## Cutout the Brain
+
+We can use AI technology to realize the division of brain regions, this process is called Segmentation, and the brain regions are as label. There are many models and methods for medical image segmentation, here we recommend [Total Segmentator](https://github.com/wasserth/TotalSegmentator).
+
+It is an out-of-the-box CT/MRI medical image segmentation Python library. You can deploy Total Segmentator locally or process your data through the official online site [https://totalsegmentator.com](https://totalsegmentator.com).
+
+Let me demonstrate the operation, we are still using the CT data from VHP. Click on "Drop DICOM.zip or ..." and select the VHP_M_CT_Head.zip file. Enter "brain" below "Selected task", press Enter to confirm. Finally click "Process data" button.
+
+![alt text](assets/advanced_usage/image-8.png)
+
+Wait for the uploading and processing, after all, you will be redirected to the following page, click "Download results" button, download and unzip segmentations.zip to any folder directory.
+
+![alt text](assets/advanced_usage/image-9.png)
+
+In case you can't get the brain segment (label), you can download it directly (without unzipping it).
+
+[VHP_M_CT_Head_Brain.nii.gz](https://drive.google.com/file/d/1KGiZ3G11YLXkGszSrvWQ9kTF4TMpxUPd/view?usp=drive_link)
+
+Next we import the brain segment (label) into the container in the same way as we imported the anatomical imagess(under the same container), but this time selecting **as Label**, with the Layer Name set to "Brain", and leaving the other options as default. Once the import is complete, a new "Fetch Layer" node will be added. Create and set the node as shown below, and you should be able to see the surface of brain.
+
+![alt text](assets/advanced_usage/image-10.png)
+
+Do you see the "step" like shape on surface? this is due to the low precision of the AI segmentation results. So How to addressing the issue? Select the Fetch Layer node of the brain label, right-click to pop up menu, click **Bioxel Nodes > Resample Value**, in the dialog box, change the "Smooth Size" to 3 or higher (the higher it is, the slower the calculation will be), click OK and wait for the calculation to finish. When it is done, a new layer will be added, replace the original one, is it smoother? The following figure lists different levels of smoothing.
+
+![alt text](assets/advanced_usage/image-11.png)
+
+The smoother it is, the more comfortable it looks, but it also means that more details are lost and the calculation is slower.
+
+But if you cut the brain, you will find that the cross-section is homogeneous, this is because the label type layer is does not contain any modality data itself. Bioxel Nodes provides a function to fill in any layer values by label, if you set the value below brain tissue in the non-brain region of CT data, then any "Cutout" node will only keep the brain region.
+
+The operation is like this, select the "Fetch Layer" node of the CT data, right-click to pop up the menu, click **Bioxel Nodes > Fill Value by Label**, in the dialog box, Label is set to the newly imported brain label layer, Fill Value is set to -100 (as long as it is less than the Hu value of the brain tissue), click OK. wait for the data processing, a new data layer will be added to the container when it is finished.
+
+As shown in the following figure, connect the nodes and set the node parameters, in which the two colors of "Set Color by Ramp 2" node are `B26267` and `EBC5B7`, and the "Cutout by Range" and "Set Color by Ramp 2" node are changed to "Value" mode (the default is "Factor" mode), because the maximum and minimum values of the brain are too close to each other and difficult to adjust in "Factor" mode. To make the brain tissue look more transparent, I add the "Set Properties" (Add > Bioxel Nodes > Property > Set Properties) node at the end and changed the "Density" to 0.5.
+
+![alt text](assets/advanced_usage/image-12.png)
+
+(Still feel not smooth enough? It's really hard to get rid of "step" surface completely at the moment)
+
+Now the brain is done. Finally we combine all together and you get something like the following.
+
+![alt text](assets/advanced_usage/image-13.png)
+
+🤗 If you've followed the docs up to this point, give yourself a round of applause, you've mastered this addon! If you have trouble following the docs instruction, you can download the project files to understand how the nodes work.
+
+[VHP.zip](https://drive.google.com/file/d/1EHB7sxNSvJNwmoTfFnpyOSsm2M_2Y0hO/view?usp=sharing)
+
+AI segmentation is an essential part of volumetric data visualization. Therefore, the future Bioxel Nodes will integrate some commonly used AI segmentation models, so that users can complete the volumetric data visualization in place!

docs/faq.md

@@ -1,4 +1,4 @@
-# Q & A
+# FAQ
 
 ## Nothing in the scene when rendering?
 

docs/step_by_step.md

@@ -2,9 +2,9 @@
 
 ## Download Data
 
-Here is the open data from [Visible Human Project (VHP)](https://www.nlm.nih.gov/research/visible/visible_human.html) for you to learn how to use this addon. It is a CT scan image of a male head, download and unzip it to any location for later usage.
+Here is the open data from [Visible Human Project (VHP)](https://www.nlm.nih.gov/research/visible/visible_human.html) for you to learn how to use this addon. It is a CT scan image of a male head, download and unzip it to a new directry for later usage.
 
-[VHP_M_CT_Head.zip](https://drive.google.com/file/d/1kcnEwwDj-5X4U8330G_FNfWFS8f-892R/view?usp=sharing)
+[VHP_M_CT_Head.zip](https://drive.google.com/file/d/1bBGpt5pQ0evr-0-f4KDNRnKPoUYj2bJ-/view?usp=drive_link)
 
 VHP original radiology data was shared in the proprietary format that predated DICOM, complicating its use. Now all data has been harmonized to standard DICOM and released in [NCI Imaging Data Commons (IDC)](https://portal.imaging.datacommons.cancer.gov/), you also can download data from IDC by yourself.
 
@@ -32,7 +32,7 @@ In the container's geometry nodes panel menu, click **Bioxel Nodes > Add a Slice
 
 The "Slicer" node is used to display the slices of the data, with an external object as the location of the slice plane. This step is not necessary for visualization, but it provides a quick way to preview the data in Blender for user perception. Next, let's turn the volumetric data into a renderable object.
 
-## Cutout the skull
+## Cutout the Skull
 
 Bone tends to have much higher CT values than soft tissue, so you can split bone and soft tissue by setting a threshold. In the Geometry Nodes panel menu of the container, click **Add > Bioxel Nodes > Component > Cutout by Threshold** to add a "Cutout" node and connect it between the "Fetch Layer" node and the "Output", and then set the "Threshold" parameter of the "Cutout by Threshold" node to 0.3 and turn on the "With Surface" option. Switch to viewport shading to "Render Preview". The node graph and the render result are shown below.
 
@@ -78,13 +78,9 @@ As shown below, you can connect "Slime Shader" node (Add > Bioxel Nodes > Surfac
 
 ![alt text](assets/step_by_step/image-7.png)
 
-If you want to edit the mesh, click **Bioxel Nodes > Extract from Container > Extract Surface** in the Geometry Nodes panel menu of the container. The addon will create a new object with Geometry Nodes, in which a "Fetch Surface" node to read the surface mesh. You can apply this geometry node to edit the mesh as usual. Or you can leave the geometry node in place and keep it connected to the original container in real time.
+If you want to edit the mesh, in the Geometry Nodes panel menu of the container, click **Bioxel Nodes > Extract from Container > Extract Mesh**, the addon will create a new mesh model object prefixed with the container name, and then you can perform the usual 3D operations, such as digitally sculpting, animating, exporting to 3D print format stl, etc.
 
-![alt text](assets/step_by_step/image-8.png)
-
-(To show it better, I made an X-axis offset to the newly created mesh, otherwise the container and the mesh are perfectly overlapping.)
-
-## Save Temporary Files
+## Deliver Blender File
 
 The layers cache is stored in a temporary folder, while the addon's custom nodes are linked to the node library file in addon directory. Both of them are exist locally by default, and if you only deliver the Blender file to other devices, the file will not work properly because the resources are missing.
 

mkdocs.yml

@@ -11,10 +11,12 @@ nav:
   - Getting Started:
       - Installation: installation.md
       - Step by Step: step_by_step.md
+      - Advanced Usage: advanced_usage.md
       - Support Format: support_format.md
       - Improve Performance: improve_performance.md
-      - Advanced: advanced.md
-      - Q & A: qa.md
+      - FAQ: faq.md
+  - Tutorials:
+      - SARS-Cov-2: SARS-Cov-2.md
 
 theme:
   name: material

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "bioxelnodes"
-version = "1.0.2"
+version = "1.0.5"
 description = ""
 authors = ["Ma Nan <icrdr2010@outlook.com>"]
 license = "MIT"