Bone Suppression on Chest Radiographs With Adversarial Learning

• URL: http://arxiv.org/abs/2002.03073v1
• Date: Sat, 8 Feb 2020 02:53:16 GMT
• Title: Bone Suppression on Chest Radiographs With Adversarial Learning
• Authors: Jia Liang, Yuxing Tang, Youbao Tang, Jing Xiao, Ronald M. Summers
• Abstract summary: Dual-energy (DE) chest radiography provides the capability of selectively imaging two clinically relevant materials. We learn the mapping between conventional radiographs and bone suppressed radiographs. We compare the effectiveness of training with patient-wisely paired and unpaired radiographs.
• Score: 21.331378067323573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 sometimes happen due to involuntary patient motion. In this work, we learn the mapping between conventional radiographs and bone suppressed radiographs. Specifically, we propose to utilize two variations of generative adversarial networks (GANs) for image-to-image translation between conventional and bone suppressed radiographs obtained by DE imaging technique. We compare the effectiveness of training with patient-wisely paired and unpaired radiographs. Experiments show both training strategies yield "radio-realistic'' radiographs with suppressed bony structures and few motion artifacts on a hold-out test set. While training with paired images yields slightly better performance than that of unpaired images when measuring with two objective image quality metrics, namely Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), training with unpaired images demonstrates better generalization ability on unseen anteroposterior (AP) radiographs than paired training.

Related papers

• Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction [83.73429628413773]
  Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth. We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN) Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
  arXiv  Detail & Related papers  (2023-12-19T22:40:51Z)
• Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis [61.089776864520594]
  We propose eye-tracking as an alternative to text reports for medical images. By tracking the gaze of radiologists as they read and diagnose medical images, we can understand their visual attention and clinical reasoning. We introduce the Medical contrastive Gaze Image Pre-training (McGIP) as a plug-and-play module for contrastive learning frameworks.
  arXiv  Detail & Related papers  (2023-12-11T02:27:45Z)
• UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields [38.62191342903111]
  We propose an Uncertainty-aware MedNeRF (UMedNeRF) network based on generated radiation fields. We show the results of CT projection rendering with a single X-ray and compare our method with other methods based on generated radiation fields.
  arXiv  Detail & Related papers  (2023-11-10T02:47:15Z)
• Low-Dose CT Image Enhancement Using Deep Learning [0.0]
  It is preferable to use as low a dose of ionizing radiation as possible, particularly in computed tomography (CT) imaging systems. A popular method for radiation dose reduction in CT imaging is known as the quarter-dose technique. Recent and popular deep-learning approaches provide an intriguing possibility of image enhancement for low-dose artifacts.
  arXiv  Detail & Related papers  (2023-10-31T08:34:33Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• Improving Computed Tomography (CT) Reconstruction via 3D Shape Induction [3.1498833540989413]
  We propose shape induction, that is, learning the shape of 3D CT from X-ray without CT supervision, as a novel technique to incorporate realistic X-ray distributions during training of a reconstruction model. Our experiments demonstrate that this process improves both the perceptual quality of generated CT and the accuracy of down-stream classification of pulmonary infectious diseases.
  arXiv  Detail & Related papers  (2022-08-23T13:06:02Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• CT Image Harmonization for Enhancing Radiomics Studies [10.643230630935781]
  RadiomicGAN is developed to mitigate the discrepancy caused by using non-standard reconstruction kernels. A novel training approach, called Dynamic Window-based Training, has been developed to transform the pre-trained model to the medical imaging domain. Model performance evaluated using 1401 radiomic features show that RadiomicGAN clearly outperforms the state-of-art image standardization models.
  arXiv  Detail & Related papers  (2021-07-03T04:03:42Z)
• Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop [63.81818077092879]
  We propose an end-to-end semi-supervised cross-modal contrastive learning framework for medical images. We first apply an image encoder to classify the chest X-rays and to generate the image features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray.
  arXiv  Detail & Related papers  (2021-04-11T09:16:29Z)
• XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors [118.27130593216096]
  A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane. To the best of our knowledge, this is the first work on radiograph view synthesis. We show that by gaining an understanding of radiography in 3D space, our method can be applied to radiograph bone extraction and suppression without groundtruth bone labels.
  arXiv  Detail & Related papers  (2020-12-04T05:08:53Z)
• Evaluation of Contemporary Convolutional Neural Network Architectures for Detecting COVID-19 from Chest Radiographs [0.0]
  We train and evaluate three model architectures, proposed for chest radiograph analysis, under varying conditions. We find issues that discount the impressive model performances proposed by contemporary studies on this subject.
  arXiv  Detail & Related papers  (2020-06-30T15:22:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.