Journal Article

RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning

Boonrod, Arunnit, Takahashi, Naoki, Philbrick, Kenneth A, Akkus, Zeynettin, Sakinis, Tomas, Erickson, Bradley J, Weston, Alexander D, Zeinoddini, Atefeh, Petro Kostandy, Kline, Timothy L, Korfiatis, Panagiotis

2019

Overview

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.

• Publisher: Springer Nature B.V
• Subject: Algorithms / Annotations / Artificial intelligence / Automation / Computer programs / Concept learning / Contours / Datasets / Deep learning / Error reduction / Feedback loops / Image annotation / Iterative methods / Learning algorithms / Medical imaging / Scientists / Shape / Software / Standardization