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. 2022 Aug;35(4):1061-1068.
doi: 10.1007/s10278-022-00608-9. Epub 2022 Mar 18.

Utilizing Synthetic Nodules for Improving Nodule Detection in Chest Radiographs

Minki Chung #  1 , Seo Taek Kong #  1 , Beomhee Park  1 , Younjoon Chung  1 , Kyu-Hwan Jung  2 , Joon Beom Seo  3
Affiliations

Utilizing Synthetic Nodules for Improving Nodule Detection in Chest Radiographs

Minki Chung et al. J Digit Imaging. 2022 Aug.

Abstract

Algorithms that automatically identify nodular patterns in chest X-ray (CXR) images could benefit radiologists by reducing reading time and improving accuracy. A promising approach is to use deep learning, where a deep neural network (DNN) is trained to classify and localize nodular patterns (including mass) in CXR images. Such algorithms, however, require enough abnormal cases to learn representations of nodular patterns arising in practical clinical settings. Obtaining large amounts of high-quality data is impractical in medical imaging where (1) acquiring labeled images is extremely expensive, (2) annotations are subject to inaccuracies due to the inherent difficulty in interpreting images, and (3) normal cases occur far more frequently than abnormal cases. In this work, we devise a framework to generate realistic nodules and demonstrate how they can be used to train a DNN identify and localize nodular patterns in CXR images. While most previous research applying generative models to medical imaging are limited to generating visually plausible abnormalities and using these patterns for augmentation, we go a step further to show how the training algorithm can be adjusted accordingly to maximally benefit from synthetic abnormal patterns. A high-precision detection model was first developed and tested on internal and external datasets, and the proposed method was shown to enhance the model's recall while retaining the low level of false positives.

Keywords: Chest radiographs; Computer-aided detection; Generative adversarial networks; Online data augmentation.

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Conflict of interest statement

Minki Chung, Seo Taek Kong, Beomhee Park, Younjoon Chung and Kyu-Hwan Jung are employees of VUNO Inc. Kyu-Hwan Jung is an equity holder of VUNO Inc.

Figures

Fig. 1
Fig. 1
Examples of synthetically generated nodular patterns: (Top) normal image templates, (middle) masks extracted from real nodular patterns, and (bottom) synthetically generated nodular cases
Fig. 2
Fig. 2
Nodular pattern generation schematic diagram. After eroding the lung templates extracted from a lung segmentation model S, the nodule synthesizer G produces artificial nodular patterns on a masked input xM. Masks retrieved from real nodular patterns are randomly placed to fit the lung template
Fig. 3
Fig. 3
Nodule synthesizer network G and its training procedure
Fig. 4
Fig. 4
Synthesized images when the nodule synthesis network was trained on (left) 250, (middle) 500, and (right) 1958 abnormal images. Synthetic examples become more visually realistic when the size of training image is increased
See this image and copyright information in PMC

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