Conference (Sort by date)

  • Wu, Y., Hong, Y., Ahmad, S., Lin, W., Shen, D., Yap, P,-T., Consortium, U.B.C.P., others, 2020. Tract Dictionary Learning for Fast and Robust Recognition of Fiber Bundles, in: In International Conference on Medical Image Computing and Computer-Assisted Intervention. (Oral Presentation, NIH Award)

  • Wu, Y., Hong, Y., Ahmad, S., Chang, W., Lin, W., Shen, D., Yap, P,-T. 2020. Globally Optimized Super-Resolution of Diffusion MRI Data via Fiber Continuity, in: In International Conference on Medical Image Computing and Computer-Assisted Intervention. (Oral Presentation, NIH Award))

  • Wu, Y., Hong, Y., Lin, W., and Yap, P.T. Tract Dictionary Learning for Fast and Robust Recognition of Fiber Bundles, 28th ISMRM, Sydney, Australia, Apr. 17-23, 2020. (Oral Presentation)

  • Wu, Y., Hong, Y., and Yap, P.T. Mitigating Gyral Bias via Active Cortex Tractography, 28th ISMRM, Sydney, Australia, Apr. 17-23, 2020. (Oral Presentation)

  • Wu, Y., Lin, W., Shen, D., Yap, P.-T., Consortium, U.B.C.P., others, 2019d. Asymmetry Spectrum Imaging for Baby Diffusion Tractography, in: International Conference on Information Processing in Medical Imaging. Springer, pp. 319–331. (Oral Presentation)

  • Wu, Y., Feng, Y., Shen, D., Yap, P.-T., 2018a. A Multi-Tissue Global Estimation Framework for Asymmetric Fiber Orientation Distributions, in: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp. 45–52. (Oral Presentation)

  • Wu, Y., Feng, Y., Li, F., Westin, C.F., 2015. Global consistency spatial model for fiber orientation distribution estimation, in: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). IEEE, pp. 1180–1183. (Oral Presentation)

  • Wu, Y., Hong, Y., Lin, W., and Yap, P.T. , the UNC/UMN Baby Connectome Project Consortium. White Matter Tract Atlases of the Baby Brain, OHBM, Montreal, Canada, June 26-July 3, 2020.

  • Wu, Y., Hong, Y., Lin, W., and Yap, P.T. Model-Free, Fast, and Automated Correction of Diffusion Gradient Orientations, 28th ISMRM, Sydney, Australia, Apr. 17-23, 2020.

  • Wu, Y., Hong, Y., Lin, W., and Yap, P.T. Automated Identification of Non-Brain Voxels for Clean Brain Extraction Using Diffusion MRI, 28th ISMRM, Sydney, Australia, Apr. 17-23, 2020.

  • Wu, Y., Feng, Y., Shen, D., Yap, P.-T., 2018c. Penalized Geodesic Tractography for Mitigating Gyral Bias, in: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp. 12–19.

  • Wu, Y., Feng, Y., Shen, D., Yap, P.-T., 2018b. Asymmetric Orientation Distributions Mitigate Gyral Bias in Cortical Tractography, in: OHBM, Singapore, 17-21 June, 2018.

  • Wu, Y., Lin, W., Shen, D., Yap, P.-T., 2019b. Improving Tractography in Baby Diffusion MRI via Asymmetric Spectrum Imaging, in: Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM).

  • Wu, Y., Lin, W., Shen, D., Yap, P.-T., 2019c. The Effects of Fiber Response Functions on Orientation Estimation in Baby Diffusion MRI, in: OHBM, Rome, Italy, June 9-13, 2019.

  • Wu, Y., Xu, Y., Feng, Y., Gao, C., Li, F., 2014. A new model-based spherical deconvolution method for multi-fiber reconstruction, in: 2014 9th IEEE Conference on Industrial Electronics and Applications. IEEE, pp. 1456–1460.

  • Zhang, F., Wu, Y., Norton, I., Rathi, A.J., Yogesh, Golby, O’Donnell, L.J., 2019a. White matter parcellation test-retest reproducibility of diffusion MRI tractography fiber clustering, in: Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM).

  • Zhang, F, Wu, Y., Norton, I., Rathi, Y., Makris, N., O’Donnell, L., 2018. A data-driven groupwise fiber clustering atlas for consistent white matter parcellation and anatomical tract identification of subjects across the lifespan, in: In: Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM).

  • Ahmad, S., Wu, Y., Huynh, K.M., Thung, K.H., Lin, W., Shen, D., Yap, P.T. and UNC/UMN Baby Connectome Project Consortium, 2020, October. Fast Correction of Eddy-Current and Susceptibility-Induced Distortions Using Rotation-Invariant Contrasts. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 34-43). Springer, Cham.

  • Huynh K., Wu, Y., Thung, K,-H., Ahmad, S., Taylor, H., Shen, D., Yap, P,-T. 2020. Characterizing Intra-Soma Diffusion with Spherical Mean Spectrum Imaging, in: International Conference on Information Processing in Medical Imaging. (Accepted)

  • Taylor, H., Ahmad, S, Wu, Y., Huynh, K., Zhou, Z., Wu, Z., Li, G., Lin, W., Wang L., Shen, D., Zhang H, Yap P.-T. “Iterative Longitudinal Infant Cortical Parcellation Using Multi-Modal Connectome Harmonics”, OHBM, Montreal, Canada, June 26- July 3, 2020.

  • Huynh, K.M., Wu, Y., Thung, K.H., Ahmad, S., Taylor, H., Lin, W., Shen, D., Yap, P.T., “Quantifying Intra-Soma Diffusion Properties via Spherical Mean Spectrum Imaging”, 28th ISMRM, Paris, France, August 7-10, 2020.

  • Huynh, K.M., Wu, Y., Taylor, H., Lin, W., Shen, D., Yap, P.T., “Tackling Degeneracy in Linear Tensor Encoding Diffusion MRI”, 28th ISMRM, Paris, France, August 7-10, 2020.

  • Huynh, K.M., Ahmad, S., Wu, Y., Thung, K.H., Wu, Z., Lin, W., Zhang, H., Wang, L., Li, G., and Yap, P.T. “Correlation of Myelin Content and Neurite Density in the Early Developing Human Cortex”, OHBM, Montreal, Canada, June 26-30, 2020.

  • Huynh, K.M., Wu, Y., Thung, K.H., Ahmad, S., Taylor, H., Lin, W., Shen, D., Yap, P.T., “Multivariate Quantification of Brain Development During the First Two Years of Life”, OHBM, Montreal, Canada, June 26-30, 2020.

  • Huynh, K.M., Wu, Y., Thung, K.-H., Chen, G., Lin, W., Shen, D., Yap, P.-T., 2019b. Biases of Microstructure Models in Baby Diffusion MRI, in: Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM).

  • Sun, P., Wu, Y., Chen, G., Wu, J., Shen, D., Yap, P.-T., 2018. Tissue Segmentation Using Sparse Non-negative Matrix Factorization of Spherical Mean Diffusion MRI Data, in: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp. 69–76.

  • Huynh, K.M., Xu, T.,Wu, Y., Thung, K.H., Chen, G., Lin, W., Shen, D. and Yap, P.T., 2019, October. Characterizing Non-Gaussian Diffusion in Heterogeneously Oriented Tissue Microenvironments. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 556-563). Springer, Cham.

  • Huynh, K.M., Xu, T.,Wu, Y., Chen, G., Thung, K.H., Wu, H., Lin, W., Shen, D., Yap, P.T. and UNC/UMN Baby Connectome Project Consortium, 2019, October. Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 547-555). Springer, Cham.

  • Taylor IV, H.P., Wu, Z.,Wu, Y., Shen, D., Zhang, H. and Yap, P.T., 2019, October. Automated Parcellation of the Cortex Using Structural Connectome Harmonics. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 475-483). Springer, Cham.

  • Huynh, K.M., Wu, Y., Chen, G., Thung, K.-H., Lin, W., Shen, D., Yap, P.-T., 2019. Quantifying Tissue Microstructure Non-Gaussianity in the Presence of Fiber Dispersion,105th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Dec. 1-6, 2019. (Oral Presentation)

  • Li, G., Liu, Y., Zheng, Y., Wu, Y., Yap, P.-T., Qiu, S., Zhang, H., and Shen, D., 2019. Multiscale Modeling of Intra-Regional and Inter-Regional Connectivities and Their Alterations in Major Depressive Disorder, 105th RSNA Scientific Assembly and Annual Meeting, Chicago, USA, Dec. 1-6, 2019. (Oral Presentation)

  • Hu, J.Q., Feng, Y.J., Zhou, S.Q., Huang, L.P., Zeng, Q.R., Wu, Y. and Li, Y.Q., 2017, May. An improved mass spring model based on internal point set domain constraint. In 2017 29th Chinese Control And Decision Conference (CCDC) (pp. 6826-6831). IEEE.

  • Li, G., Liu, Y., Zheng, Y., Wu, Y., Yap, P.T., Qiu, S., Zhang, H. and Shen, D., 2019, October. Identification of Abnormal Circuit Dynamics in Major Depressive Disorder via Multiscale Neural Modeling of Resting-State fMRI. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 682-690). Springer, Cham.

  • Jiang, W., Zhang, H., Hsu, L.M., Hu, D., Li, G., Wu, Y. and Shen, D., 2019, October. Early Development of Infant Brain Complex Network. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 832-840). Springer, Cham.

  • Jiao, Z., Huang, P., Kam, T.E., Hsu, L.M., Wu, Y., Zhang, H. and Shen, D., 2019, October. Dynamic Routing Capsule Networks for Mild Cognitive Impairment Diagnosis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 620-628). Springer, Cham.

  • Liu, F., Feng, J., Chen, G., Wu, Y., Hong, Y., Yap, P.T. and Shen, D., 2019. DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 620-628). Springer, Cham.

  • Cao, Z., Jin, E., Zhou, S., Wu, Y., Li, Y. and Feng, Y., 2018, May. A Data-driven Voxel-wise White Matter Fiber Clustering Model Based on Priori Anatomical Data. In 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) (pp. 65-70). IEEE.

  • Xiao, C., Feng, Y., Li, Y., Zeng, Q., Zhang, J. and Wu, Y., 2017, May. Real-time and authentic blood simulation for surgical training. In 2017 29th Chinese Control And Decision Conference (CCDC) (pp. 6832-6837). IEEE.

  • Gao, C., Feng, Y., Wu, Y., Zhang, J., Xu, T. and Wang, Z., 2016, July. Swarm tracking approach for global probabilistic tractography with spherical deconvolution. In 2016 35th Chinese Control Conference (CCC) (pp. 4048-4053). IEEE.

  • Zhang, J., Xu, T., Feng, Y., Wu, Y., Li, Y., He, J. and Zhou, S., 2016, May. A self-adaptive local feature extraction based magnetic resonance imaging. In 2016 Chinese Control and Decision Conference (CCDC) (pp. 6563-6567). IEEE.

  • Hong, Y., Chang, W.-T., Chen, G., Wu, Y., Lin, W., Shen, D., Yap, P.-T. “50-Fold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE)”, 28th ISMRM, Sydney, Australia, Apr. 17-23, 2020.

  • Huynh, H., Wu, Y., Thung, KH., Chen, G., Lin, W., Shen, D., Yap, P.-T. “Dense Mapping of Microstructural Development in the Human Brain During the First Two Years of Life”, OHBM, Rome, Italy, June 9-13, 2019.

  • Li, G., Liu, Y., Zheng, Y., Wu, Y., Yap, P.-T, Qiu, S. Zhang, H., Shen, D. “Aberrant Limbic-Executive Rather Than Default Mode-Salience System in Major Depressive Disorder”, OHBM, Montreal, Canada, June 26- July 3, 2020

  • Chiara Maffei, Gabriel Girard Kurt G. Schilling, Nagesh Adluru, Dogu Baran Aydoğan, Andac Hamamci, Fang-Cheng Yeh, Matteo Mancini, Ye Wu, Alessia Sarica, Achille Teillac, Steven H. Baete Davood Karimi, Ying-Chia Lin Fernando Boada Nathalie Richard Bassem Hiba, Aldo Quattrone, Yoonmi Hong, Dinggang Shen, Pew-Thian Yap, Tommy Boshkovski, Jennifer S. W. Campbell, Nikola Stikov, G. Bruce Pike, Barbara B. Bendlin, Vivek Prabhakaran, Andrew L. Alexander, Adam Anderson, Bennett A Landman Erick J. Canales-Rodríguez, Muhamed Barakovic Jonathan Rafael-Patino, Thomas Yu, Gaëtan Rensonnet Simona Schiavi Alessandro Daducci, Marco Pizzolato, Elda Fischi-Gomez Jean-Philippe Thiran George Dai, Giorgia Grisot, Nikola Lazovski, Albert Puente, Matt Rowe, Irina Sanchez, Vesna Prchkovska, Robert Jones, Julia Lehman, Suzanne Haber, Anastasia Yendiki. “The IronTract challenge: Validation and optimal tractography methods for the HCP diffusion acquisition scheme”, 28th ISMRM, Paris, France, August 7-10, 2020.

  • Wu, Y., Ahmad, S., Yap, P,-T., 2021. Highly Reproducible Whole Brain Parcellation in Individuals via Voxel Annotation with Fiber Clusters, in: International Conference on Medical Image Computing and Computer-Assisted Intervention.

  • Wu, Y., Hong, Y., Ahmad, S., Yap, P,-T., Consortium, U.B.C.P., others, 2021. Active Cortex Tractography, in: International Conference on Medical Image Computing and Computer-Assisted Intervention.

  • Wu, Y., Ahmad, S., Huynh, K.M., Liu, S., Thung, KH., Lin, W., P.-T. Yap, 2021. An Automated Processing Pipeline for Diffusion MRI in the Baby Connectome Project, ISMRM, May 15-20, 2021.

  • Wu, Y., Ahmad, S., Ma, L., Yang, E., P.-T. Yap, 2021. Subsampling Diffusion Gradients via Poisson Sphere Elimination, ISMRM, May 15-20, 2021.

  • Wu, Y., Ahmad, S., Lin, W., Yap, P.-T., 2021. White Matter Tract Atlases of a Century of Human Life, in: OHBM, Virtual Meeting, 21-25 June, 2021.

Repository Layout

The repository layout is pretty standard for a Python project, with a few quirks due to the need for compiling Sass and JS files.

  • CODE_OF_CONDUCT.md

  • LICENSE

  • README.md

  • .nox/ – Generated by nox.

  • dist/ – Generated as part of the release process.

  • docs/ – Sources for the documentation.

  • src/

    • furo/ – actual source code for the package

      • __init__.py – Handles interaction with Sphinx and some configuration.

      • navigation.py – Generates the sidebar navigation HTML.

      • sphinxext.py – Defines the internal-only furo-demo directive.

      • assets/ – contains Sass and JS source code.

      • theme/ – the folder that Sphinx adds to template lookup.

        • furo/ – main Sphinx theme folder

          • static/ – contains compiles CSS and JS code.

          • everything else here – the underlying HTML templates.

  • gulpfile.js – for Gulp.

  • noxfile.py – for nox.

  • package.json – for NPM.

  • pyproject.toml – for Python Packaging.

Theme build process

Furo’s build process uses Gulp. Running gulp build in the repository root will compile the theme’s CSS and JS assets (src/furo/assets/) into the correct final files (inside src/furo/theme/furo/static).

When building the distributions for upload, gulp build is run once and the src/furo/assets/ directory is excluded for the final distribution. Thus, both the source distribution and wheel distribution do not contain the original source code for Furo and only contain the compiled SCSS and JS files.

{note} It is not ideal that the version-controlled source tree is not installable using pip directly. There is a need for a gulp build` command to be run between the clone and installation.

Things are set up this way due to the lack of a “build” step support in Flit. There is an open issue for enhancement with a proposal awaiting feedback. `

How stuff works

Contents sidebar

This uses the toc variable that’s provided by Sphinx. There’s some CSS to hide the top-level bullet referring to this page’s title. The rest of the tree is stylised without much complexity.

The fancy bit is that Gumshoe.js is used to highlight the currently active heading (which heading is the content at the top pixel under). This is combined with some custom JS to scroll to the currently active heading in the toctree, which results in the contents sidebar scrolling with the user as they go.

CSS variables for customisation

This is pretty much the “USP” of this theme. In src/furo/theme/furo/partials/_head_css_variables.html, the user provided CSS variables are translated and written into each page’s HTML. This is set up, such that these declarations overrides any other declarations made in the CSS of the theme.

This essentially allows the user to control the values of the CSS variables, and hence control how the theme looks.

furo-demo directive

This directive was written to make it easier to write the examples used in the Reference section. The way it works is pretty straightforward really:

  • It only works in MyST documents, since it performs an in-place substitution.

  • It takes the contents of the block, splits it at “+++” into Markdown and reStructuredText snippets.

  • For both of these, it renders a tab that has a code block containing the snippet followed by the actual code itself.

    • This is carefully crafted to ensure that things are evaluated correctly.

This approach has significant limitations however, since the A/B comparision format means that it is not directly usable to showcase functionality that is different between the two.

For that, we keep one of the snippets empty, which thanks to a conditional, results in the tab for that language (MyST or reST) not being rendered.