TY - JOUR
T1 - RIL-Contour
T2 - a Medical Imaging Dataset Annotation Tool for and with Deep Learning
AU - Philbrick, Kenneth A.
AU - Weston, Alexander D.
AU - Akkus, Zeynettin
AU - Kline, Timothy L.
AU - Korfiatis, Panagiotis
AU - Sakinis, Tomas
AU - Kostandy, Petro
AU - Boonrod, Arunnit
AU - Zeinoddini, Atefeh
AU - Takahashi, Naoki
AU - Erickson, Bradley J.
N1 - Publisher Copyright:
© 2019, The Author(s).
PY - 2019/8/15
Y1 - 2019/8/15
N2 - Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to “learn” from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort to curate these datasets is widely regarded as a barrier to the development of deep-learning systems. We developed RIL-Contour to accelerate medical image annotation for and with deep-learning. A major goal driving the development of the software was to create an environment which enables clinically oriented users to utilize deep-learning models to rapidly annotate medical imaging. RIL-Contour supports using fully automated deep-learning methods, semi-automated methods, and manual methods to annotate medical imaging with voxel and/or text annotations. To reduce annotation error, RIL-Contour promotes the standardization of image annotations across a dataset. RIL-Contour accelerates medical imaging annotation through the process of annotation by iterative deep learning (AID). The underlying concept of AID is to iteratively annotate, train, and utilize deep-learning models during the process of dataset annotation and model development. To enable this, RIL-Contour supports workflows in which multiple-image analysts annotate medical images, radiologists approve the annotations, and data scientists utilize these annotations to train deep-learning models. To automate the feedback loop between data scientists and image analysts, RIL-Contour provides mechanisms to enable data scientists to push deep newly trained deep-learning models to other users of the software. RIL-Contour and the AID methodology accelerate dataset annotation and model development by facilitating rapid collaboration between analysts, radiologists, and engineers.
AB - Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to “learn” from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort to curate these datasets is widely regarded as a barrier to the development of deep-learning systems. We developed RIL-Contour to accelerate medical image annotation for and with deep-learning. A major goal driving the development of the software was to create an environment which enables clinically oriented users to utilize deep-learning models to rapidly annotate medical imaging. RIL-Contour supports using fully automated deep-learning methods, semi-automated methods, and manual methods to annotate medical imaging with voxel and/or text annotations. To reduce annotation error, RIL-Contour promotes the standardization of image annotations across a dataset. RIL-Contour accelerates medical imaging annotation through the process of annotation by iterative deep learning (AID). The underlying concept of AID is to iteratively annotate, train, and utilize deep-learning models during the process of dataset annotation and model development. To enable this, RIL-Contour supports workflows in which multiple-image analysts annotate medical images, radiologists approve the annotations, and data scientists utilize these annotations to train deep-learning models. To automate the feedback loop between data scientists and image analysts, RIL-Contour provides mechanisms to enable data scientists to push deep newly trained deep-learning models to other users of the software. RIL-Contour and the AID methodology accelerate dataset annotation and model development by facilitating rapid collaboration between analysts, radiologists, and engineers.
KW - Annotation by iterative deep learing (AID)
KW - Classification
KW - Deep-learning
KW - Medical image annotation
KW - Segmentation
KW - Software tools
UR - http://www.scopus.com/inward/record.url?scp=85066018289&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066018289&partnerID=8YFLogxK
U2 - 10.1007/s10278-019-00232-0
DO - 10.1007/s10278-019-00232-0
M3 - Article
C2 - 31089974
AN - SCOPUS:85066018289
SN - 0897-1889
VL - 32
SP - 571
EP - 581
JO - Journal of Digital Imaging
JF - Journal of Digital Imaging
IS - 4
ER -