Re:Show HN: DICOM Medical Image Parser in Go
by jacquesm on Sunday June 30, 2019 @12:19AM
(#20316304)
Super, thank you for the detailed answer. I'll be sure to point a couple of companies to your repository, they may be able to contribute in various ways.
Re:Show HN: DICOM Medical Image Parser in Go
by suyashkumar on Sunday June 30, 2019 @05:16PM
(#20320572)
Of course, and sweet thanks! Open to ideas and contribs as well!
Re:Show HN: DICOM Medical Image Parser in Go
by kspacewalk on Saturday June 29, 2019 @09:12PM
(#20315782)
Where would you use the functionality of converting DICOM into protocol buffers?
Re:Show HN: DICOM Medical Image Parser in Go
by suyashkumar on Saturday June 29, 2019 @09:37PM
(#20315862)
Perhaps sending structured slices of dicoms to downstream microservices (or clients), Tensorflow inputs, perhaps in other places too? Proto representations of data can be nice because they are super easy to unpack (way easier than a dicom directly) and offer language-native data containers to work with the data. But ultimately you could always just deal with the dicom, or store the data in some other representation (JSON, etc).
The goal here is to have a featured implementation in Go that's easy to work with as a library and easy to iterate on and add new features on top of. This library unpacks most standard elements and metadata inside the dicom, in addition to multiple image frames that may be stored in the dicom in a variety of formats. More utils for normalizing and working with these images are coming soon! Also will be looking forward to dumping the dicom data into protocol buffers (that I also helped generate at github.com/gradienthealth/dicom-protos).
As for the forking--I did a lot of work on the parent fork (which isn't maintained anymore) and decided to introduce a lot of API breaking changes and opinionated new features on my actively maintained fork so decided to move ahead with a hard fork (with all the git history and credit maintained). You can see the original author's blessing here: https://www.reddit.com/r/golang/comments/bnu47l/high_perform...