Bone suppression on chest radiographs with adversarial learning

  title={Bone suppression on chest radiographs with adversarial learning},
  author={Jia Wen Liang and Yuxing Tang and Youbao Tang and Jing Xiao and Ronald M. Summers},
  booktitle={Medical Imaging},
Dual-energy (DE) chest radiography provides the capability of selectively imaging two clinically relevant materials, namely soft tissues, and osseous structures, to better characterize a wide variety of thoracic pathology and potentially improve diagnosis in posteroanterior (PA) chest radiographs. However, DE imaging requires specialized hardware and a higher radiation dose than conventional radiography, and motion artifacts some- times happen due to involuntary patient motion. In this work, we… 

Applying a Conditional GAN for Bone Suppression in Chest Radiography Images

This work proposed an alternative to solve the bone suppression task in chest radiography images using Generative Adversarial Networks (GANs), and used a conditional GAN type (cGAN) to provide a bone-suppressed version of the initial image.

Value of bone suppression software in chest radiographs for improving image quality and reducing radiation dose

Bone suppression software achieves superior image quality for lung lesions than dual-energy subtraction technique in bone suppression images and can decrease the radiation dose over the hardware-based image processing technique.

Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen.

SPIE Computer-Aided Diagnosis conference anniversary review.

The topics and trends observed in research presented at the Computer-Aided Diagnosis conference as part of the 50th-anniversary celebration of SPIE Medical Imaging are described.

Autoencoder-based bone removal algorithm from x-ray images of the lung

  • Seweryn KaliszM. Marczyk
  • Computer Science
    2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)
  • 2021
A deep learning model using convolutional denoising autoencoder architecture was developed to remove ribs from chest X-ray images, and the resulting images are characterized by partial or complete suppression of the ribs.



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

This paper introduces 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 chestX-rays and integrates a conditional generative adversarial network that complements the traditional regression method minimizing the pairwise image difference.

Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

A multitask deep learning model is introduced that generates simultaneously the bone-suppressed image and the organ-segmented image, enhancing the accuracy of tasks, minimizing the number of parameters needed by the model and optimizing the processing time, all by exploiting the interplay between the network parameters to benefit the performance of both tasks.

When Does Bone Suppression And Lung Field Segmentation Improve Chest X-Ray Disease Classification?

This contribution investigates the usefulness of two advanced image pre-processing techniques, initially developed for image reading by radiologists, for the performance of Deep Learning methods for chest radiography.

Bone suppression technique for chest radiographs

The bone-suppression technology significantly improved radiologists’ performance in the detection of CT-confirmed possible nodules and pneumothoraces on chest radiographs and showed that radiologists were more confident in making diagnoses regarding the presence or absence of an abnormality after rib-suppressed companion views were presented.

Improved detection of focal pneumonia by chest radiography with bone suppression imaging

Use of bone suppression images improved radiologists’ performance for detection of focal pneumonia on chest radiographs and can improve the accuracy of radiologists in detecting focal pneumonia.

Dual Energy Subtraction and Temporal Subtraction Chest Radiography

Temporal subtraction is a complementary technique that enhances interval change, by using a previous radiograph as a subtraction mask, so that unchanged normal anatomy is suppressed, whereas new abnormalities are enhanced.

CT-realistic data augmentation using generative adversarial network for robust lymph node segmentation

Experimental results on NIH lymph node dataset demonstrate that the proposed data augmentation approach can produce realistic CT images and the lymph node segmentation performance is improved effectively using the additional augmented data, e.g. the Dice score increased about 2.2%.

Bone Suppression Increases the Visibility of Invasive Pulmonary Aspergillosis in Chest Radiographs

The detection of IPA in CXRs can be improved when their evaluation is aided by bone suppressed images, and BSI improved the sensitivity of the CXR examination, outweighing a small loss in specificity.

XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation

A criss-cross attention based segmentation network and radiorealistic chest X-ray image synthesis for data augmentation are proposed and the lung masks of synthetic abnormal CXRs are propagated from the segmentation results of their normal counterparts, and then serve as pseudo masks for robust segmentor training.