Keywords: Public Dataset, Instance Segmentation, Rib, CXR, Deep Learning
TL;DR: Introducing a benchmark dataset and baseline deep learning models for segmentation and labeling of individual ribs on chest x-rays
Abstract: Segmenting and labeling correctly the individual ribs from chest radiograph (CXR) are of significant clinical value for several diagnostic tasks. Developing automatic deep learning (DL) algorithms for this task requires annotated images of the ribs at pixel-level. However, to the best of our knowledge, there exists no such public datasets as well as benchmark protocols for performance evaluation. To solve this problem, we establish a new CXR dataset, namely VinDr-RibCXR, for automatically segmenting and labeling of individual ribs. The VinDr-RibCXR contains 245 posteroanterior CXRs with corresponding segmentation annotations for each rib provided by human experts. Furthermore, we train the state-of-the-art DL-based segmentation models on 196 images from the RibCXR and report performance of those models on an independent test set of 49 images. Our best performing DL model (i.e., Nested U-Net with EfficientNet-B0) obtains a Dice score of 0.834 (95% CI, 0.810-0.853). The sensitivity, specificity and Hausdorff distance are 0.841 (95% CI, 0.812-0.858), 0.998 (95% CI, 0.997-0.998), and 15.453 (95% CI, 13.340-17.450), respectively. These results demonstrate a high-level of performance in labeling of the individual ribs from CXRs. Our study, therefore, serves as a proof of concept and baseline performance for future research. The dataset, codes, and trained DL models will be made publicly available to encourage new advances in this research direction.
Paper Type: both
Primary Subject Area: Segmentation
Secondary Subject Area: Application: Radiology
Paper Status: original work, not submitted yet
Source Code Url: The codes for training deep learning models will be made publicly available through our Github project at https://github.com/vinbigdata-medical/MIDL2021-VinDr-RibCXR/ upon the publication of the paper.
Data Set Url: The VinDr-RibCXR dataset will be made publicly available through our project website at https://vindr.ai/. We also plan to open it on PhysioNet (https://physionet.org/) upon the publication of the paper.
Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.