[{"data":1,"prerenderedAt":211},["ShallowReactive",2],{"project-25250589":3},{"id":4,"name":5,"line":6,"line_source":7,"title":8,"url":9,"domain":10,"maker":11,"posted_at":12,"year":13,"points":14,"comments":15,"rank_in_year":16,"shown_in_year":17,"top":18,"tier":19,"category":20,"subcategory":21,"format":22,"keywords":23,"reception":28,"sentiment":29,"stance_mix":30,"scored_comments":31,"aspects":33,"strip":36,"post_text":29,"what":38,"said":49,"maker_in_thread":102,"shape":111,"grid":115,"attempts":175,"same_week":176,"maker_shows":31,"maker_hidden":204,"reader_version":205,"card":206},"25250589","Dicom","Dicom Medical Image Library in Go","title","Show HN: Dicom Medical Image Library in Go","https:\u002F\u002Fgithub.com\u002Fsuyashkumar\u002Fdicom\u002Ftree\u002Fv1.0.0","github.com","suyashkumar",1606708610,2020,108,24,599,3028,20,"notable","languages","medical imaging library","library",[24,25,26,27],"dicom","medical imaging","go library","dicom parser","quiet",null,{"positive":31,"neutral":32,"mixed":32,"critical":32},3,0,{"idea":34},{"positive":35,"critical":32},1,[32,32,32,32,32,32,32,32,37,37,32,32,32,32,32,32,32,32,32,37,32,32,32,32],5,{"problem":29,"audience":29,"pricing":29,"motivation":29,"time_to_build":29,"stack":39,"openness":46,"stage":47,"team_size":48},[40],{"value":41,"source":42,"evidence":43},"Go","inferred",[44],{"id":45},"post","open_source","launched","unknown",{"praise":50,"objections":71,"requests":72,"questions":80,"alternatives":86},[51],{"value":52,"source":53,"evidence":54},"Great work and useful library for DICOM parsing","commenter",[55,59,63,67],{"id":56,"by":57,"text":58},"25250981","raphexion_","Congratulation. Great work. It is such an ancient but important standard. I just spent around one year writing Wolfpacs, a dicom load balancer in pure Erlang. (In my spare time).…",{"id":60,"by":61,"text":62},"25252148","FL33TW00D","Great work! I've used dcm2niix[1] many times in the past to get around using DICOM directly, perhaps this will make it easier in future. [1] https:\u002F\u002Fgithub.com\u002Frordenlab\u002Fdcm2niix",{"id":64,"by":65,"text":66},"25251289","sleepy_keita","This is really good. I've been using pydicom (https:\u002F\u002Fgithub.com\u002Fpydicom\u002Fpydicom) but it's always good to have variety in libraries and tools.",{"id":68,"by":69,"text":70},"25254743","cridenour","I wrote a DICOM parser in Go for the Kaggle competition in 2017, and it was such a pain. It probably only worked for that sample so I never released it but this looks great.…",[],[73],{"value":74,"source":53,"evidence":75},"Support for parsing context-dependent elements like Native PixelData via sequence lookback",[76,77],{"id":56,"by":57,"text":58},{"id":78,"by":11,"text":79},"25254683","Thanks for the kind words! I will definitely check out wolfpacs. As for your question: I've run into a couple scenarios that require \"lookback\" into the parsed Dataset to parse…",[81],{"value":82,"source":53,"evidence":83},"How to handle undecided VR headers such as signed or unsigned short",[84,85],{"id":56,"by":57,"text":58},{"id":78,"by":11,"text":79},[87,91,95],{"value":88,"source":53,"evidence":89},"pydicom",[90],{"id":64,"by":65,"text":66},{"value":92,"source":53,"evidence":93},"dcm2niix",[94],{"id":60,"by":61,"text":62},{"value":96,"source":53,"evidence":97},"Orthanc",[98],{"id":99,"by":100,"text":101},"25252807","tecleandor","Nice! I'll take a look to it. Usually, for something like that, I would use Orthanc plus a bit of Python or Lua scafolding. What advantages does WolfPACS provides?",{"comments":31,"answered":31,"key":103},[104,105,108],{"id":78,"text":79},{"id":106,"text":107},"25254598","You may be able to find some longitudinal data sets at The Cancer Imaging Archive: https:\u002F\u002Fwww.cancerimagingarchive.net\u002F (some are also already preloaded into Google Cloud…",{"id":109,"text":110},"25254569","Indeed, there are some good sources of public, deidentified medical images released under various generally permissive licenses. 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