Enhanced Dicom fix, Image preprocessing

This is a bugfix release that solves a problem in the loading of advanced dicom.
It also incorporates some changes in the dafne_dl package which allow for more flexible image normalization options.

Small bugfix release

This release fixes some crashes during usage.

1.8-alpha

Wow, this update really feels like magic! Being able to just improve your ROIs with a single click, it's the dream right?

Well, it's now possible thanks to the integration into Dafne of the Segment Anything Model (SAM)

Minor bug fixes

Minor bug fixes in the display of images

3D viewer and other improvements

With this release, following shortly the previous one, we brought some feature enhancements. The most notable ones are:

• 3D visualization of the anatomy and the active ROI
• Slice-wise statistics

Precompiled binaries for windows and Mac-ARM (coming soon) are provided. Please use the pip installation for Linux and Mac-Intel.

Memory improvements and new features

In this release, we have now dramatically reduced Dafne's memory footprint by compressing the masks on the non-displayed slices in-memory. This allows for typical reductions of more than 10-fold, with negligible lag.

As other improvements, now the deep learning autosegmentation can be applied to a subregion of the image (for example to only segment one limb when it's not centered in the FOV), and a "Mixed" option for the model source was added, to have both remote and local models available at the same time.

As a downside, the compiled Linux and MacOS-Intel versions don't seem to be working properly. For these platforms, dafne should be installed through pip.

New model browser - ready for a big model zoo!

This release sees minor bugfixes and updated libraries in the distribution, but most importantly, it now features a "Model browser". Dafne is ready to host many new segmentation models!

A spine model is already available, and a kidney model is coming soon. Enable the new models from the model browser, accessible from the "Tools" menu.

1.3-alpha2

In this release, some small modification in the image preprocessing pipeline before segmentation are introduced, and the performance is improved in case of highly inhomogeneous signal. I recommend upgrading for best incremental learning performance.

Mac arm64 version coming in the next few days.

1.3-alpha

This is the first binary release after a major refactoring of the code for pip installation.
If you are familiar with Python, I recommend installing dafne with pip with

pip install dafne

The Mac versions seem to work now. Please download the one corresponding to your architecture.

Noticeable changes

• Mask interpolation. When a slice above and below are segmented, Dafne can interpolate the ROI using simple interpolation, registration-based interpolation, or a combination of both.
• New mask to contour algorithm. This algorithm produces much simpler contours. However, it is slower than the previous one.
• More adaptable UI.

New installer for Windows, Linux, and Mac

This is a release that unifies and improves the installation packages. Now the Mac installation package is signed and can be installed like a normal Mac application. A Linux stand-alone executable is provided. Just download the dafne_linux_1.1-alpha7 file and execute it directly from Linux.

All binary distributions, including the Mac one, are for the x64 architecture.

New logfile system

The output is now saved by default to log files. They are visible from within the interface from the menu Help -> Show Logs.
When reporting a bug, please include the log files called dafne_output.log and dafne_error.log (make sure that no sensitive information is contained inside!). The log files can be found at the following locations:

• Windows: C:\Users\<user>\AppData\Local\Dafne-imaging\Dafne\Cache
• Mac: /Users/<user>/Library/Caches/Dafne/
• Linux: /home/<user>/.cache/Dafne/