Translate documentation to Chinese, add support format details, and enhance styling with new fonts. Update mkdocs configuration for multilingual support and improve package dependencies.
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# Advanced Usage
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# 高级用法
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## Concepts
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## 概念
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In the previous section, I mentioned some key concepts, I'll describe them in detail so that you can better understand the following operations.
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在上一节中，我提到了一些关键概念，这里我将详细描述它们，以便您更好地理解后续操作。
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### Container
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### 容器
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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.
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首先是 **容器 (Container)**，它最容易描述，您可以将其想象为一个场景，或一个求解器容器。它是承载所有内容的舞台。
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### Layer
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### 图层
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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.
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其次是 **图层 (Layer)**，它表示同一容器中同一对象在不同位置的不同数据字段。就像任何地图应用中的视图图层，一个显示道路，一个显示卫星图，而在 Bioxel 中，一个显示 MRI，一个显示 CT，一个显示电信号等。有些图层用于材质，如颜色、密度等，我们称它们为 **材质图层 (Material Layer)**。一个容器包含同一对象的不同图层。任何导入容器的外部数据都会转换为图层。
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### Component
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### 组件
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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.
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最后是 **组件 (Component)**，它是 **材质图层** 的一个片段。同一区域中可能存在多个具有不同呈现意义的材质（物质），就像 Blender 中的对象，每个代表场景的一部分。Bioxel Nodes 提供了一系列节点和工具，允许用户基于其他图层（作为源数据）创建材质图层（组件）。在上一节中，我们使用 CT 数据（图层）来制作颅骨的材质（物质），这个过程称为**材质化 (materialization)**。
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## Attache the Muscles to the Skull
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## 将肌肉附着到颅骨
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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.
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一个容器中可以存在多个组件，就像一个场景中可以存在多个对象一样。它们可以单独控制，也可以相互组合以构建复杂的场景。接下来，让我们从 VHP 解剖图像中提取肌肉，并将其附着到上一节创建的颅骨上。解剖图像非常大，为了便于使用，我将其裁剪并缩放到原始大小的 1/4。下载文件并解压到新文件夹。
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[VHP_M_AnatomicalImages_Head.zip](https://drive.google.com/file/d/164AjQX0tmgUpWZlleJ5i1IKHco4ytDu2/view?usp=drive_link)
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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)
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您也可以直接从官方网站下载数据：[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)
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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.
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首先按[分步指南](step_by_step.md)中所述导入 CT 数据。然后导入解剖图像，但与之前不同的是，这次从现有容器的几何节点菜单（不是顶部菜单）中点击 **Bioxel Nodes > Import Volumetric Data (Init) > as Color**，定位到解压后的文件夹，选择任意一个 .png 文件（不要多选，也不要选择文件夹）。这样导入的图层将与之前的 CT 数据位于同一容器中。您也可以通过将其拖入容器几何节点界面而不是 3D 视图界面来导入。另外请注意，点击的是 **as Color**（而不是 as Scalar），因为解剖图像是 RGB 数据。
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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**.
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这次让我们在导入选项上多花点时间。首先谈谈选项中的 **Original Spacing** 是什么，您可以简单地将其理解为图像的原始"像素大小"。由于体数据是 3D 的，其像素（即体素）是长方体而不是 2D 矩形，它们有长度、宽度和高度，这就是 **Spacing**。
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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)`.
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通常，Bioxel Nodes 可以从数据头文件中读取间距，您不需要填写该值，但任何 PNG 图像都没有记录该信息，因此您需要手动输入。在 VHP 的官方说明中，解剖图像的每个像素对应 0.33mm 的大小，每个切片的厚度为 1mm，因此解剖图像的 Original Spacing 应设置为 `(0.33, 0.33, 1)`。
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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)`)
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Layer Name should be set to "Anatomical Images", other options remain default, click Ok.
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但是，我提供的文件已将每个图像缩放了 1/4，因此 X 和 Y 轴需要乘以 4。所以最终的 Original Spacing 应设置为 `(1.33, 1.33, 1)`，这应该很容易理解，对吧？（如果您导入的是官方文件，则仍应设置为 `(0.33, 0.33, 1)`）
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Layer Name 应设置为 "Anatomical Images"，其他选项保持默认，点击确定。
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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.
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导入完成后，在新创建的 "Fetch Layer" 节点之后连接 "Cutout by Hue" 节点（Add > Bioxel Nodes > Component > Cutout by Hue），并打开 Slice Viewer 模式预览，如下图所示。
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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.
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除了间距，Dicom 文件还记录了对象的位置和方向，但这些信息显然没有记录在 PNG 格式中。这导致 CT 数据和解剖图像在位置上不匹配。我们需要手动对齐这两个图层：首先，将 "Cutout by Threshold" 节点的阈值设置为 0.2，以便可以提取并显示完整的头部。然后在 "Cutout by Hue" 节点和 "Cutout by Threshold" 节点之前各添加一个 "ReCenter" 节点，然后在"解剖图像"工作流程中添加 Locater（详见[分步指南](step_by_step.md)），最后添加 "Join Component" 节点（Add > Bioxel Nodes > Component > Join Component），并按如下图所示连接和设置参数。
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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.
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"Join Component" 节点允许您将两个组件合并在一起。移动和旋转 Locater 对象，使两个组件的位置完全一致。如果您难以对齐，以下是 Locater 对象属性的精确变换值。
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Next let's cutout the muscles individually.
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接下来让我们分别提取肌肉。
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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.
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首先谈谈 "Cutout by Hue" 节点。它的名称表明通过颜色色调提取区域。如果您选择此节点并按 "M" 键暂时静音它，您会看到数据在 Slice Viewer 模式下变成一个带有蓝色部分的立方体。"Cutout by Hue" 节点的默认 Hue 值是红色，这意味着该节点保留接近红色的区域，并去除蓝色（它在色调环上与红色相对），留下人体部分。
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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).
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如果调低 Sensitivity 的值，您会看到可见部分减少，只留下更接近红色的部分，而肌肉恰好是红色的，因此我们可以轻松提取肌肉。标本组织的颜色不如活体状态那样鲜艳，我们可以使用 "Set Color by Color" 节点（Add > Bioxel Nodes > Property > Set Color by Color）来微调颜色以恢复活力。
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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.
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最后，按如下图所示连接和设置节点，其中 "Set Color by Ramp 2" 节点中的两个颜色是 `E7D2C5` 和 `FFEDEC`。如果一切正常，您将获得如下图所示的渲染结果。
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You can also add "Transfrom" and "Cutter" nodes at the end and all components.
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您还可以在末尾和所有组件上添加 "Transform" 和 "Cutter" 节点。
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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.
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VHP 的多模态数据没有任何变形或缩放差异，只有位置差异，因此所有数据都可以轻松对齐。然而，在大多数情况下，不同模态之间的差异非常大，需要先进行配准，目前 Bioxel Nodes 尚不支持（但在计划中）。尽管如此，您可以基于同一图层构建不同的组件并将它们组合在一起。例如，基于 CT 数据，使用 "Cutout by Range" 节点可以消除低值区域（脂肪），消除高值区域（骨骼），仍能得到肌肉。然后将其附着到颅骨上。
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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?
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然而，大脑和肌肉的 CT 值非常接近，因此通过阈值将大脑与肌肉分离极其困难。那么应该如何提取大脑呢？
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## Cutout the Brain
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## 提取大脑
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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).
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我们可以使用 AI 技术来实现大脑区域的划分，这个过程称为**分割 (Segmentation)**，大脑区域称为**标签 (Label)**。医学图像分割有很多模型和方法，这里推荐 [Total Segmentator](https://github.com/wasserth/TotalSegmentator)。
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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).
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这是一个开箱即用的 CT/MRI 医学图像分割 Python 库。您可以在本地部署 Total Segmentator，也可以通过官方在线网站 [https://totalsegmentator.com](https://totalsegmentator.com) 处理您的数据。
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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.
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让我演示一下操作，我们仍然使用 VHP 的 CT 数据。点击 "Drop DICOM.zip or..." 并选择 VHP_M_CT_Head.zip 文件。在 "Selected task" 下方输入 "brain"，按 Enter 确认。最后点击 "Process data" 按钮。
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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.
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等待上传和处理，之后您将被重定向到以下页面，点击 "Download results" 按钮，下载并解压 segmentations.zip 到任意文件夹。
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In case you can't get the brain segment (label), you can download it directly (without unzipping it).
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如果您无法获取大脑分割（标签），可以直接下载（无需解压）。
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[VHP_M_CT_Head_Brain.nii.gz](https://drive.google.com/file/d/1KGiZ3G11YLXkGszSrvWQ9kTF4TMpxUPd/view?usp=drive_link)
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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.
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接下来，我们以与导入解剖图像相同的方式将大脑分割（标签）导入到容器中（在同一容器下），但这次选择 **as Label**，Layer Name 设置为 "Brain"，其他选项保持默认。导入完成后，会添加一个新的 "Fetch Layer" 节点。按如下图所示创建和设置节点，您应该能够看到大脑表面。
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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.
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您看到表面上有"台阶"状的形状了吗？这是因为 AI 分割结果精度较低。那么如何解决这个问题？选择大脑标签的 Fetch Layer 节点，右键弹出菜单，点击 **Bioxel Nodes > Resample Value**，在对话框中，将 "Smooth Size" 更改为 3 或更高（数值越高，计算越慢），点击确定并等待计算完成。完成后，会添加一个新的图层，替换原来的图层，它更平滑了吗？下图列出了不同平滑级别的效果。
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The smoother it is, the more comfortable it looks, but it also means that more details are lost and the calculation is slower.
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越平滑，看起来越舒服，但这也意味着丢失更多细节，计算也更慢。
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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.
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但是，如果您切开大脑，会发现截面是均匀的，这是因为标签类型图层本身不包含任何模态数据。Bioxel Nodes 提供了一种通过标签填充任意图层值的功能，如果您将 CT 数据中非大脑区域的脑组织值设置得低于大脑组织的 Hu 值，那么任何 "Cutout" 节点将只保留大脑区域。
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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.
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操作如下：选择 CT 数据的 "Fetch Layer" 节点，右键弹出菜单，点击 **Bioxel Nodes > Fill Value by Label**，在对话框中，Label 设置为新导入的大脑标签图层，Fill Value 设置为 -100（只要小于大脑组织的 Hu 值即可），点击确定。等待数据处理，完成后会向容器添加一个新的数据图层。
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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.
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如下图所示，连接节点并设置节点参数，其中 "Set Color by Ramp 2" 节点的两个颜色是 `B26267` 和 `EBC5B7`，"Cutout by Range" 和 "Set Color by Ramp 2" 节点改为 "Value" 模式（默认是 "Factor" 模式），因为大脑的最大值和最小值太接近，在 "Factor" 模式下难以调整。为了使脑组织看起来更透明，我在末尾添加了 "Set Properties"（Add > Bioxel Nodes > Property > Set Properties）节点并将 "Density" 改为 0.5。
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(Still feel not smooth enough? It's really hard to get rid of "step" surface completely at the moment)
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（仍然觉得不够平滑？目前很难完全摆脱"台阶"表面。）
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Now the brain is done. Finally we combine all together and you get something like the following.
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现在大脑完成了。最后，我们将所有内容组合在一起，您将得到如下图所示的结果。
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🤗 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.
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如果您按照文档走到了这一步，为自己鼓掌吧，您已经掌握了这个插件！如果您按照文档操作有困难，可以下载项目文件来理解节点的工作方式。
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[VHP.zip](https://drive.google.com/file/d/1EHB7sxNSvJNwmoTfFnpyOSsm2M_2Y0hO/view?usp=sharing)
|
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[VHP.zip](https://drive.google.com/file/d/1EHB7sxNSvJNwmoTfFnpyOSsm2M_2Y0hO/view?usp=drive_link)
|
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|
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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!
|
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AI 分割是体数据可视化的重要组成部分。因此，未来的 Bioxel Nodes 将集成一些常用的 AI 分割模型，让用户可以就地完成体数据可视化！
|
||||
Reference in New Issue
Block a user