Dilated conditional GAN for bone suppression in chest radiographs with enforced semantic features

Abstract

Purpose The purpose of this essay is to improve computer‐aided diagnosis of lung diseases by the removal of bone structures imagery such as ribs and clavicles, which may shadow a clinical view of lesions. This paper aims to develop an algorithm to suppress the imaging of bone structures within clinical x‐ray images, leaving a residual portrayal of lung tissue; such that these images can be used to better serve applications, such as lung nodule detection or pathology based on the radiological reading of chest x rays. Methods We propose a conditional Adversarial Generative Network (cGAN) (Mirza and Osindero, Conditional generative adversarial nets, 2014.) model, consisting of a generator and a discriminator, for the task of bone shadow suppression. The proposed model utilizes convolutional operations to expand the size of the receptive field of the generator without losing contextual information while downsampling the image. It is trained by enforcing both the pixel‐wise intensity similarity and the semantic‐level visual similarity between the generated x‐ray images and the ground truth, via optimizing an L‐1 loss of the pixel intensity values on the generator side and a feature matching loss on the discriminator side, respectively. Results The framework we propose is trained and tested on an open‐access chest radiograph dataset for benchmark. Results show that our model is capable of generating bone‐suppressed images of outstanding quality with a limited number of training samples (N = 272). Conclusions Our approach outperforms current state‐of‐the‐art bone suppression methods using x‐ray images. Instead of simply downsampling images at different scales, our proposed method mitigates the loss of contextual information by utilizing dilated convolutions, which gains a noticeable quality improvement for the outputs. On the other hand, our experiment shows that enforcing the semantic similarity between the generated and the ground truth images assists the adversarial training process and achieves better perceptual quality.

Deep Learning for Chest X-ray Analysis: A Survey

“…Most of the papers surveyed in this work train and test their method on data from the same domain. This finding is inline with the previously reported studies (Kim et al, 2019; Conditional GAN and two variational autoencoders designed for CXR generation PR Gomi et al (2020) Novel reconstruction algorithm for CXR enhancement PR Zhou et al (2020b) Bone shadow suppression using conditional GANS with dilated U-Net variant BS J Matsubara et al (2020) Generates CXRs from CT to train CNN for bone suppression BS PR Zunair and Hamza (2021) Generates COVID-19 CXR images to improve network training and performance CV CC,RP Bayat et al (2020) 2D-to-3D encoder-decoder network for generating 3D spine models from CXR studies Z PR Bigolin Lanfredi et al (2020) Generates normal from abnormal CXRs, uses the deformations as disease evidence Z PR Prevedello et al, 2019) and highlights an important concern: most of the performance levels reported in the literature might not generalize well to data from other domains (Zech et al, 2018). Several studies (Yao et al, 2019;Zech et al, 2018;Cohen et al, 2020b) demonstrated that there was a significant drop in performance when deep learning systems were tested on datasets outside their training domain for a variety of CXR applications.…”

“…(Gozes and Greenspan, 2019;Baltruschat et al, 2019a). More sophisticated pre-processing steps to improve model performance include bone suppression (Baltruschat et al, 2019b;Zhou et al, 2020b) and lung cropping (Liu et al, 2019).…”

“…The most widely studied subject in the image generation lit-erature is image enhancement. Several researchers investigated bone suppression (Liu et al, 2020a;Matsubara et al, 2020;Zarshenas et al, 2019;Gozes and Greenspan, 2020;Lin et al, 2020;Zhou et al, 2020b) and lung enhancement Gozes and Greenspan, 2020) techniques to improve image interpretability. A number of works (Liu et al, 2020a;Zhou et al, 2020b) employed GANs to generate bone-suppressed images.…”

“…Several researchers investigated bone suppression (Liu et al, 2020a;Matsubara et al, 2020;Zarshenas et al, 2019;Gozes and Greenspan, 2020;Lin et al, 2020;Zhou et al, 2020b) and lung enhancement Gozes and Greenspan, 2020) techniques to improve image interpretability. A number of works (Liu et al, 2020a;Zhou et al, 2020b) employed GANs to generate bone-suppressed images. For example, Liu et al (2020a) employed GANs and leveraged additional input to the generator to guide the dual-energy subtraction (DES) soft-tissue image generation process.…”

Bone Suppression on Chest Radiographs for Pulmonary Nodule Detection: Comparison between a Generative Adversarial Network and Dual-Energy Subtraction

“…In the present study, we used a bone suppression model based on the GAN framework to maintain the similarity between the target and generation domains [ 20 ]. The GAN can optimize image generators by adding discriminators as assisting components during training sessions [ 29 ]. To prevent image blurring, our model was designed to learn the frequency details of original images more effectively through the Harr 2D wavelet decomposition of the input data.…”