Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus

<div class="floats-full-page floats-full-page-figure"><h2>Figure 4</h2><div class="floats-full-page-caption"><div>Modified 3D UNet. The 3D CT scan is fed into a UNet, with the standard downsampling and upsampling layers. The layer with aggregated probability maps improves the performance of the network for the ventricle and especially for the subarachnoid class.</div></div><div class="floats-full-page-content"><table class="figure-group"><tr class="fig-image"><td><object data="https://d31iiq90dwlpfy.cloudfront.net/articles/bmef/volume-2022/9783128/figures/9783128.fig.004.svgz" name="9783128.fig.004" type="image/svg+xml"></object></td></tr></table></div></div>

Figure 4 | Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus

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Figure 4: Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus

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Figure 4

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<p>The open access journal <em>BME Frontiers (</em><em>BMEF)</em>, published in association with SIBET CAS, is a platform for the multidisciplinary community of biomedical engineering, publishing wide-ranging research in the field.</p>


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<p><em>BMEF's</em> editorial board is led by Xingde Li&nbsp;(Johns Hopkins University), Yuguo Tang&nbsp;(Suzhou Institute of Biomedical Engineering and Technology), and&nbsp;Guoqi Zhang&nbsp;(Delft University of Technology) and is comprised of leading experts in the field of biomedical engineering.</p>


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The open access journal BME Frontiers (BMEF), published in association with SIBET CAS, is a platform for the multidisciplinary community of biomedical engineering, publishing wide-ranging research in the field.