TAG-SPARK/README.html at main · chenwunci/TAG-SPARK · GitHub

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    <title>
        TAG-SPARK - HackMD
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    <div id="doc" class="markdown-body container-fluid comment-enabled" data-hard-breaks="false" style=""><h1 id="TAG-SPARK-TAG-lens-based-SPAtial-redundancy-driven-noise-Reduction-Kernel" data-id="TAG-SPARK-TAG-lens-based-SPAtial-redundancy-driven-noise-Reduction-Kernel" style=""><a class="anchor hidden-xs" href="#TAG-SPARK-TAG-lens-based-SPAtial-redundancy-driven-noise-Reduction-Kernel" title="TAG-SPARK-TAG-lens-based-SPAtial-redundancy-driven-noise-Reduction-Kernel"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>TAG-SPARK: TAG-lens-based SPAtial redundancy-driven noise Reduction Kernel</span></h1><p><span>Our work has been published in Advanced Science. </span><a href="https://doi.org/10.1002/advs.202405293" title="Title" target="_blank" rel="noopener"><strong><span>paper</span></strong></a></p><h2 id="Contents" data-id="Contents" style=""><a class="anchor hidden-xs" href="#Contents" title="Contents"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>Contents</span></h2><ul>
<li><a href="#Overview"><span>Overview</span></a></li>
<li><a href="#DirectoryStructure"><span>Directory Structure</span></a></li>
<li><a href="#PythonCode"><span>Python Code</span></a></li>
<li><a href="#Results"><span>Results</span></a></li>
<li><a href="#Citation"><span>Citation</span></a></li>
</ul><h2 id="Overview" data-id="Overview" style=""><a class="anchor hidden-xs" href="#Overview" title="Overview"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>Overview</span></h2><p><span>Two-photon high-speed fluorescence calcium imaging stands as a mainstream technique in neuroscience for capturing neural activities with high spatiotemporal resolution. However, challenges arise from the inherent tradeoff between acquisition speed and image quality, grappling with a low signal-to-noise ratio (SNR) due to limited signal photon flux. </span><em><strong><span>Here, a contrast-enhanced video-rate volumetric system, integrating a tunable acoustic gradient (TAG) lens-based high-speed microscopy with a TAG-SPARK denoising algorithm is demonstrated.</span></strong></em><br>
<span>The former facilitates high-speed dense z-sampling at sub-micrometer-scale intervals, allowing the latter to exploit the spatial redundancy of z-slices for self-supervised model training. </span><em><strong><span>This spatial redundancy-based approach, tailored for 4D (xyzt) dataset, not only achieves &gt;700% SNR enhancement but also retains fast-spiking functional profiles of neuronal activities.</span></strong></em><br>
<span>High-speed plus high-quality images are exemplified by in vivo Purkinje cells calcium observation, revealing intriguing dendritic-to-somatic signal convolution, i.e., similar dendritic signals lead to reverse somatic responses. </span><em><strong><span>This tailored technique allows for capturing neuronal activities with high SNR, thus advancing the fundamental comprehension of neuronal transduction pathways within 3D neuronal architecture.</span></strong></em>
<img src="https://hackmd.io/_uploads/H1RHlkxFkx.jpg" alt="advs9491-fig-0001-m" class="offline-handled error-handled" loading="lazy"></p><h2 id="Directory-Structure" data-id="Directory-Structure" style=""><a class="anchor hidden-xs" href="#Directory-Structure" title="Directory-Structure"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>Directory Structure</span></h2><div class="code-block-wrapper"><div class="code-toolbar"><button class="rounded text-normal font-normal leading-normal flex bg-transparent text-text-default border border-solid border-transparent hocus:bg-element-bg-hover hocus:text-text-emphasize hover:border-element-bg-hover focus:shadow-[0_0_0_2px_#77777733] focus:border-element-border-hover disabled:bg-transparent disabled:hocus:bg-transparent disabled:hocus:border-transparent disabled:text-element-text-disabled disabled:hocus:text-element-text-disabled ui-code-block-copy-button p-[7px]" data-state="closed"><i class="inline-flex ph ph-clipboard-text" aria-hidden="true" style="width: 20px; height: 20px; font-size: 20px; line-height: 20px;"></i></button></div>
        <pre><code>TAG-SPARK
|---- datasets
|---- |---- noisy images (project name)
|---- |---- |---- VOL_1.tiff
|---- |---- |---- VOL_2.tiff
|---- deepcad
|---- |---- buildingblocks.py
|---- |---- dataprocess.py
|---- |---- model_3DUnet.py
|---- |---- network.py
|---- |---- test_collection.py
|---- |---- train_collection.py
|---- |---- utils.py
|---- onnx
|---- |---- model (model name)
|---- |---- |---- model.onnx
|---- pth
|---- |---- model (model name)
|---- |---- |---- model.pyh
|---- |---- |---- model.yaml
|---- results
|---- |---- denoised images (project name)
|---- |---- |---- VOL_1_denoised.tiff
|---- |---- |---- VOL_2_denoised.tiff
|---- demo_train_pipeline.py
|---- demo_test_pipeline.py
</code></pre>
      </div><h2 id="Python-Code" data-id="Python-Code" style=""><a class="anchor hidden-xs" href="#Python-Code" title="Python-Code"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>Python Code</span></h2><h3 id="Updates" data-id="Updates" style=""><a class="anchor hidden-xs" href="#Updates" title="Updates"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>Updates</span></h3><p><img class="emoji offline-handled error-handled" alt=":pushpin:" src="https://cdn.jsdelivr.net/npm/@hackmd/emojify.js@2.1.0/dist/images/basic/pushpin.png"><span> ver.1, 2023, Kai-Chun Jhan</span></p><h3 id="Our-environment" data-id="Our-environment" style=""><a class="anchor hidden-xs" href="#Our-environment" title="Our-environment"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>Our environment</span></h3><ul>
<li><span>Windows 10</span></li>
<li><span>Python 3.9</span></li>
<li><span>Pytorch 1.8.0</span></li>
<li><span>NVIDIA GPU (GeForce RTX 3080) + CUDA (12.2)</span></li>
</ul><p><span>How to install CUDA/Cudnn</span><br>
<a href="https://medium.com/geekculture/install-cuda-and-cudnn-on-windows-linux-52d1501a8805" target="_blank" rel="noopener"><span>https://medium.com/geekculture/install-cuda-and-cudnn-on-windows-linux-52d1501a8805</span></a></p><h3 id="Setup" data-id="Setup" style=""><a class="anchor hidden-xs" href="#Setup" title="Setup"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>Setup</span></h3><p><strong><span>1. Install CUDA</span></strong><br>
<span>Ensure that CUDA is installed and properly configured for your system.</span><br>
<strong><span>2. Create a Virtual Environment</span></strong><br>
<span>Set up a Python virtual environment (Python ≥ 3.9).</span><br>
<strong><span>3. Clone the Repository and Install Dependencies</span></strong><br>
<span>Clone the repository and install the required dependencies, including PyTorch.</span></p><div class="code-block-wrapper"><div class="code-toolbar"><button class="rounded text-normal font-normal leading-normal flex bg-transparent text-text-default border border-solid border-transparent hocus:bg-element-bg-hover hocus:text-text-emphasize hover:border-element-bg-hover focus:shadow-[0_0_0_2px_#77777733] focus:border-element-border-hover disabled:bg-transparent disabled:hocus:bg-transparent disabled:hocus:border-transparent disabled:text-element-text-disabled disabled:hocus:text-element-text-disabled ui-code-block-copy-button p-[7px]" data-state="closed"><i class="inline-flex ph ph-clipboard-text" aria-hidden="true" style="width: 20px; height: 20px; font-size: 20px; line-height: 20px;"></i></button></div>
        <pre><code>$ conda create -n tagspark python=3.9
$ conda activate tagspark
$ pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
$ pip install -r requirements.txt
</code></pre>
      </div><h3 id="Training" data-id="Training" style=""><a class="anchor hidden-xs" href="#Training" title="Training"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>Training</span></h3><p><span>A sample dataset is available in the datasets folder. Activate the virtual environment and navigate to the project directory before running </span><code>demo_train_pipeline.py</code><span>. Once completed, you can proceed with training the TAG-SPARK model. To use a custom dataset, update the </span><code>datasets_path</code><span> variable accordingly.</span></p><div class="code-block-wrapper"><div class="code-toolbar"><button class="rounded text-normal font-normal leading-normal flex bg-transparent text-text-default border border-solid border-transparent hocus:bg-element-bg-hover hocus:text-text-emphasize hover:border-element-bg-hover focus:shadow-[0_0_0_2px_#77777733] focus:border-element-border-hover disabled:bg-transparent disabled:hocus:bg-transparent disabled:hocus:border-transparent disabled:text-element-text-disabled disabled:hocus:text-element-text-disabled ui-code-block-copy-button p-[7px]" data-state="closed"><i class="inline-flex ph ph-clipboard-text" aria-hidden="true" style="width: 20px; height: 20px; font-size: 20px; line-height: 20px;"></i></button></div>
        <pre><code>$ conda activate tagspark
$ python demo_train_pipeline.py
</code></pre>
      </div><h3 id="Testing" data-id="Testing" style=""><a class="anchor hidden-xs" href="#Testing" title="Testing"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>Testing</span></h3><p><span>Run </span><code>demo_test_pipeline.py</code><span> to evaluate the pre-trained model's denoising performance on the sample dataset. To test with your own data and a retrained model, update the </span><code>datasets_path</code><span> and </span><code>denoise_model</code><span> variables accordingly.</span></p><div class="code-block-wrapper"><div class="code-toolbar"><button class="rounded text-normal font-normal leading-normal flex bg-transparent text-text-default border border-solid border-transparent hocus:bg-element-bg-hover hocus:text-text-emphasize hover:border-element-bg-hover focus:shadow-[0_0_0_2px_#77777733] focus:border-element-border-hover disabled:bg-transparent disabled:hocus:bg-transparent disabled:hocus:border-transparent disabled:text-element-text-disabled disabled:hocus:text-element-text-disabled ui-code-block-copy-button p-[7px]" data-state="closed"><i class="inline-flex ph ph-clipboard-text" aria-hidden="true" style="width: 20px; height: 20px; font-size: 20px; line-height: 20px;"></i></button></div>
        <pre><code>python demo_test_pipeline.py
</code></pre>
      </div><h3 id="Param-explanation" data-id="Param-explanation" style=""><a class="anchor hidden-xs" href="#Param-explanation" title="Param-explanation"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>Param explanation</span></h3><ul>
<li><strong><span>demo_train_pipeline.py</span></strong></li>
</ul><div class="code-block-wrapper"><div class="code-toolbar"><button class="rounded text-normal font-normal leading-normal flex bg-transparent text-text-default border border-solid border-transparent hocus:bg-element-bg-hover hocus:text-text-emphasize hover:border-element-bg-hover focus:shadow-[0_0_0_2px_#77777733] focus:border-element-border-hover disabled:bg-transparent disabled:hocus:bg-transparent disabled:hocus:border-transparent disabled:text-element-text-disabled disabled:hocus:text-element-text-disabled ui-code-block-copy-button p-[7px]" data-state="closed"><i class="inline-flex ph ph-clipboard-text" aria-hidden="true" style="width: 20px; height: 20px; font-size: 20px; line-height: 20px;"></i></button></div>
        <pre><code class="python hljs"><div class="wrapper"><div class="gutter linenumber"><span></span>
<span></span>
<span></span>
<span></span>
<span></span>
<span></span>
<span></span>
<span></span>
<span></span></div><div class="code"><span class="hljs-comment"># %% Select file(s) to be processed</span>

datasets_path = <span class="hljs-string">f'datasets/29_4_2_4'</span>  <span class="hljs-comment"># folder containing tif files for training</span>

<span class="hljs-comment"># %% First setup some parameters for training</span>
n_epochs = <span class="hljs-number">10</span>               <span class="hljs-comment"># the number of training epochs</span>
GPU = <span class="hljs-string">'0'</span>                   <span class="hljs-comment"># the index of GPU used for computation (e.g. '0', '0,1', '0,1,2')</span>
pth_dir = <span class="hljs-string">'./pth'</span>           <span class="hljs-comment"># pth file and visualization result file path</span>
num_workers = <span class="hljs-number">0</span>             <span class="hljs-comment"># if you use Windows system, set this to 0.</span>
</div></div></code></pre>
      </div><ul>
<li><strong><span>demo_test_pipeline.py</span></strong></li>
</ul><div class="code-block-wrapper"><div class="code-toolbar"><button class="rounded text-normal font-normal leading-normal flex bg-transparent text-text-default border border-solid border-transparent hocus:bg-element-bg-hover hocus:text-text-emphasize hover:border-element-bg-hover focus:shadow-[0_0_0_2px_#77777733] focus:border-element-border-hover disabled:bg-transparent disabled:hocus:bg-transparent disabled:hocus:border-transparent disabled:text-element-text-disabled disabled:hocus:text-element-text-disabled ui-code-block-copy-button p-[7px]" data-state="closed"><i class="inline-flex ph ph-clipboard-text" aria-hidden="true" style="width: 20px; height: 20px; font-size: 20px; line-height: 20px;"></i></button></div>
        <pre><code class="python hljs"><div class="wrapper"><div class="gutter linenumber"><span></span>
<span></span>
<span></span>
<span></span>
<span></span>
<span></span>
<span></span>
<span></span></div><div class="code"><span class="hljs-comment"># %% Select file(s) to be processed (download if not present)</span>

datasets_path = <span class="hljs-string">f'datasets/29_4_2_4'</span>  <span class="hljs-comment"># folder containing tif files for testing</span>
denoise_model = <span class="hljs-string">f'29_4_2_4_202310260959'</span>  <span class="hljs-comment"># A folder containing pth models to be tested</span>

<span class="hljs-comment"># %% First setup some parameters for testing</span>
GPU = <span class="hljs-string">'0'</span>                             <span class="hljs-comment"># the index of GPU used for computation (e.g. '0', '0,1', '0,1,2')</span>
num_workers = <span class="hljs-number">0</span>                       <span class="hljs-comment"># if you use Windows system, set this to 0.</span>
</div></div></code></pre>
      </div><h2 id="Results" data-id="Results" style=""><a class="anchor hidden-xs" href="#Results" title="Results"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>Results</span></h2><p><strong><span>1. Spatial and Temporal Characterization of the Volumetric TAG-SPARK Imaging</span></strong>
<img src="https://hackmd.io/_uploads/ryJDxkgtke.jpg" alt="advs9491-fig-0002-m" class="offline-handled error-handled" loading="lazy"></p><p><strong><span>2. TAG-SPARK Facilitates Calcium Dynamics Analysis of Extensive PCs Populations</span></strong>
<img src="https://hackmd.io/_uploads/SyqDe1gK1e.jpg" alt="advs9491-fig-0003-m" class="offline-handled error-handled" loading="lazy"></p><p><span>More demo images are presented at the bottom of the </span><a href="https://doi.org/10.1002/advs.202405293" title="Title" target="_blank" rel="noopener"><span>webpage</span></a><span> as </span><em><strong><span>Supporting Information</span></strong></em><span>.</span></p><h2 id="Citation" data-id="Citation" style=""><a class="anchor hidden-xs" href="#Citation" title="Citation"><span class="octicon octicon-link ph ph-link-simple-horizontal"></span></a><span>Citation</span></h2><p><span>If you use this code, please cite the companion paper where the original method appeared:</span></p><ul>
<li><span>Yin-Tzu Hsieh, Kai-Chun Jhan, Jye-Chang Lee, et al. TAG-SPARK: Empowering High-Speed Volumetric Imaging With Deep Learning and Spatial Redundancy. Advance Science. (2024). </span><a href="https://doi.org/10.1002/advs.202405293" target="_blank" rel="noopener"><span>https://doi.org/10.1002/advs.202405293</span></a></li>
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