DicomAnnotator: a Configurable Open-Source Software Program for Efficient DICOM Image Annotation.
Modern, supervised machine learning approaches to medical image classification, image segmentation, and object detection usually require many annotated images. As manual annotation is usually labor-intensive and time-consuming, a well-designed software program can aid and expedite the annotation pro...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 6; pp. 1514 - 1527 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | diagnostic images pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147529124&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147529124 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: 2020 vid: 33 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 147529124 144558396 147529124 147529124 10.1007/s10278-020-00370-w 147529124 ppf: 1514 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: DicomAnnotator: a Configurable Open-Source Software Program for Efficient DICOM Image Annotation. aug: au: Dong, Qifei Luo, Gang Haynor, David O'Reilly, Michael Linnau, Ken Yaniv, Ziv Jarvik, Jeffrey G. Cross, Nathan affil: Department of Biomedical Informatics and Medical Education, University of Washington, 98195, Seattle, WA, USA sug: subj: Software Design DICOM Machine Learning Image Processing, Computer Assisted Data Curation Radiographic Image Enhancement ab: Modern, supervised machine learning approaches to medical image classification, image segmentation, and object detection usually require many annotated images. As manual annotation is usually labor-intensive and time-consuming, a well-designed software program can aid and expedite the annotation process. Ideally, this program should be configurable for various annotation tasks, enable efficient placement of several types of annotations on an image or a region of an image, attribute annotations to individual annotators, and be able to display Digital Imaging and Communications in Medicine (DICOM)-formatted images. No current open-source software program fulfills these requirements. To fill this gap, we developed DicomAnnotator, a configurable open-source software program for DICOM image annotation. This program fulfills the above requirements and provides user-friendly features to aid the annotation process. In this paper, we present the design and implementation of DicomAnnotator. Using spine image annotation as a test case, our evaluation showed that annotators with various backgrounds can use DicomAnnotator to annotate DICOM images efficiently. DicomAnnotator is freely available at https://github.com/UW-CLEAR-Center/DICOM-Annotator under the GPLv3 license. pubtype: Academic Journal doctype: diagnostic images pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|