Deep learning for pneumothorax detection and localization using networks fine-tuned with multiple institutional datasets

Jennie Crosby; Thomas Rhines; Feng Li; Heber MacMahon; Maryellen Giger

doi:10.1117/12.2549709

Translator Disclaimer

16 March 2020 Deep learning for pneumothorax detection and localization using networks fine-tuned with multiple institutional datasets

Jennie Crosby, Thomas Rhines, Feng Li, Heber MacMahon, Maryellen Giger

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113140C (2020) https://doi.org/10.1117/12.2549709
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Pneumothorax, presenting as a fine line at the edge of the lung and a change in texture outside the lung, is a particularly difficult condition to detect on chest radiographs due to its wide range of sizes and subtle visual signs. Deep learning methods can be applied to chest radiographs to assist in the detection and localization of pneumothorax. The visual signs of pneumothorax are usually unable to be seen at typical neural network input sizes (256 x 256 or 224 x 224); therefore, increasing the resolution of the input images is expected to be beneficial for deep learning detection of pneumothorax. In this work, chest radiographs were separated into two apex images (top third of lung) and then 256 x 256 patches were extracted from the apex images. VGG19 neural networks were fine-tuned for the task of distinguishing between images with and without pneumothorax. One network was fine-tuned with the apex images (downsized to 256 x 256) and another fine-tuned with 256 x 256 patches within the apex images. These fine-tuned networks were tested on an independent test set and ROC analysis performed. The apex-based network yielded an AUC of 0.80 (95% confidence interval (CI): 0.79, 0.81) and the patch-based network yielded an AUC of 0.73 (95% CI: 0.71, 0.74) in the task of distinguishing between images with and without pneumothorax. When the outputs from the two networks were merged via soft voting, a statistically significant increase in performance was observed as compared to either network alone (AUC=0.83, p<0.001).

Conference Presentation

Citation Download Citation

Jennie Crosby, Thomas Rhines, Feng Li, Heber MacMahon, and Maryellen Giger "Deep learning for pneumothorax detection and localization using networks fine-tuned with multiple institutional datasets", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140C (16 March 2020); https://doi.org/10.1117/12.2549709

ACCESS THE FULL ARTICLE