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Releases: khanlab/hippunfold

1.5.2 - bugfix release for T1T2w model and dentate curvature

09 Dec 04:03
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🐛 Bug Fixes

  • FIX: hipp curvature was being used always, instead of wildcard @akhanf (#321)
  • FIX: input images when using T1T2w model @akhanf (#320)

v1.5.1

30 Jul 19:13
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Cumulative changes in testing of dependencies and dockerfile

What's Changed

Full Changelog: v1.5.0...v1.5.1

1.5.0

22 Jul 17:57
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💪 Changes

  • use naturalneighbour interpolation instead of linear barycentric @jordandekraker (#306). This generally improves the quality of unfolded-folded warps and fixes some issues of misplaced vertices in subject-folded space (especially common in the DG and anterior/posterior tips)

🚀 Features

  • Templateseg workflow instead of UNet. This method is meant for cases with not much training data or with low inter-subject variability, such as non-human or neonatal data.
    • bigbrain templateseg (#309, #304)
    • dHCP templateseg(#307)
    • ABAv3 mouse atlas+template (#299)
    • initial docs for templateseg (#292)
    • Template inject Lflip vs R bug fix (#310)
  • macaque subfields atlas added atlas (#297)
  • Wet-run test data Documentation (#303) see also #311

1.4.1

20 Sep 19:04
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🐛 Bug Fixes

  • Fix session bug, where BIDS parsing was faulty when subjects had different sessions @akhanf (#261)

1.4.0

16 Sep 14:59
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🚀 Features

  • rename histologyReference2023 to multihist7 @akhanf (#256)

📝 Documentation

1.3.3

10 Aug 18:55
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🐛 Bug Fixes

  • fixes subfields label.gii bug introduced in unfold_reg by reverting @akhanf (#255)

1.3.2

03 Aug 17:03
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🐛 Bug Fixes

  • replace use of HTTP.remote() with wget @akhanf (#251)

📝 Documentation

1.3.1

03 Aug 01:20
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🧰 Maintenance

  • Download models inside workflow @akhanf (#247)
  • added neonate_v2 model, minor workflow update for it @akhanf (#249)

📝 Documentation

v1.3.0: unfolded registration

23 Jul 14:49
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Major changes (full Methods here):

  • 🎉 Inter-subject alignment is now further refined using registration in unfolded space!
  • 🚀 The default subfield parcellation scheme is now based on the maxprob of seven histology samples.
  • Together these changes align subfield boundaries to within approximately 0.5mm!
  • 💻 Can (optionally) specify alternative U-Net tissue segmentation models, including contrast-agnositic synthseg (#244)

Minor chages:

  • 🐛 Bug fix for inner/outer surfaces sometimes being pushed outside of hippocampus (#236)
  • 🐛 Curvature is no longer calculated on a smoothed surface, and is instead normalized via a tanh function (#242)
  • 📝 Improvements to Documentation clarity

Notes:

  • The effects of inter-subject alignment are most evident at high resolution, such as 7T, ex-vivo scanning, or histology. However this will still improve alignment of in-vivo scans, leading to sharper group-averaged images. Check out our full evaluation here
  • The new default subfield parcellation scheme includes the previous labelling from 3D BigBrain with some added corrections. Previous labelling schemes can optionally also be applied.
  • The new synthseg models should be considered experimental. These should be applicable to any scanning modality and sometimes to 3D histology. They also provide better segmentation detail, leading to clearer definition of digitations/gyrifications. However, these models have not yet been formally evaluated, and may stil undergo additional training iterations.
  • Check out our associated toolbox for helpful Python/Matlab analysis tools, tutorials, and examples!

Contributors

1.2.1

05 Mar 16:04
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Changes

🚀 Features

  • add cli arg for specifying crop-native-res in mm, default as 0.2mm iso @jordandekraker (#224)

🐛 Bug Fixes

📝 Documentation