Learning Bone Suppression from Dual Energy Chest X-rays using Adversarial Networks

by   Dong Yul Oh, et al.

Suppressing bones on chest X-rays such as ribs and clavicle is often expected to improve pathologies classification. These bones can interfere with a broad range of diagnostic tasks on pulmonary disease except for musculoskeletal system. Current conventional method for acquisition of bone suppressed X-rays is dual energy imaging, which captures two radiographs at a very short interval with different energy levels; however, the patient is exposed to radiation twice and the artifacts arise due to heartbeats between two shots. In this paper, we introduce a deep generative model trained to predict bone suppressed images on single energy chest X-rays, analyzing a finite set of previously acquired dual energy chest X-rays. Since the relatively small amount of data is available, such approach relies on the methodology maximizing the data utilization. Here we integrate the following two approaches. First, we use a conditional generative adversarial network that complements the traditional regression method minimizing the pairwise image difference. Second, we use Haar 2D wavelet decomposition to offer a perceptual guideline in frequency details to allow the model to converge quickly and efficiently. As a result, we achieve state-of-the-art performance on bone suppression as compared to the existing approaches with dual energy chest X-rays.


page 1

page 6

page 7

page 10

page 12

page 13

page 14

page 17


Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays

Knowledge of what spatial elements of medical images deep learning metho...

Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network

Dual-energy (DE) chest radiographs provide greater diagnostic informatio...

Informative sample generation using class aware generative adversarial networks for classification of chest Xrays

Training robust deep learning (DL) systems for disease detection from me...

Bone Suppression on Chest Radiographs With Adversarial Learning

Dual-energy (DE) chest radiography provides the capability of selectivel...

Abnormal Chest X-ray Identification With Generative Adversarial One-Class Classifier

Being one of the most common diagnostic imaging tests, chest radiography...

Generative-based Airway and Vessel Morphology Quantification on Chest CT Images

Accurately and precisely characterizing the morphology of small pulmonar...

A Deep Generative Approach to Oversampling in Ptychography

Ptychography is a well-studied phase imaging method that makes non-invas...

Please sign up or login with your details

Forgot password? Click here to reset