Bone suppression in Chest X-rays: A deep survey

🏖️Introduction

This homepage lists some representative papers/codes/datasets all about bone suppression. We aim to constantly update the latest relevant papers and help the community track this topic. We also share with you a comprehensive understanding of diffusion models in detail. Please feel free to join us and contribute to the project. If you have any questions, please feel free to contact Zhanghao Benny Chan and Yifei Sun.

Zhanghao Chen's email address is: [email protected]

Yifei Sun's email address is: [email protected]

🐈Bone Suppression Papers

We summarize each bone suppression and diffusion models based papers' highlights, and you can view them through the link below if you like.

https://docs.google.com/presentation/d/1JTPUMrqcJ3FQLsop_rtFtiW4e7onhxoK/edit?usp=sharing&ouid=107677953599522928486&rtpof=true&sd=true

The mainstream method of bone suppression is deep learning, and can be roughly classified into 5 + 1 (manual suppression) categories roughly, namely autoencoder, domain adaptation, distillation learning, GAN, convolutional neural networks. We also add a list of papers of bone suppression applications like calcification, segmentation, and detection. More details are listed in https://docs.google.com/spreadsheets/d/1Ip1XWtMDotmN-i82OPshxOWj1SnD7Ncl/edit?usp=sharing&ouid=107677953599522928486&rtpof=true&sd=true

Autoencoder

Deep Feature Disentanglement Learning for Bone Suppression in Chest Radiographs

Chunze Lin; Ruixiang Tang; Darryl D. Lin; Langechuan Liu; Jiwen Lu; Yunqiang Chen; Dashan Gao; Jie Zhou

ISBI 2020. [PDF] [Dataset for JSRT bone suppression]
Autoencoder-based bone removal algorithm from x-ray images of the lung

Seweryn Kalisz; Michal Marczyk

BIBE 2021 [PDF] [Dataset for COVID-19 X-Ray] [Dataset for JSRT bone suppression]

Distillation Learning

Bone suppression of lateral chest x-rays with imperfect and limited dual-energy subtraction images

Yunbi Liu; Fengxia Zeng; Mengwei Ma c; Bowen Zheng; Zhaoqiang Yun; Genggeng Qin; Wei Yang; Qianjin Feng

Computerized Medical Imaging and Graphics 2023. [PDF]

Domain Adaptation

From 3D to 2D: Transferring knowledge for rib segmentation in chest X-rays

Hugo Oliveira; Virginia Mota; Alexei M.C. Machado; Jefersson A. dos Santos

Pattern Recognition Letters 2020. [PDF]
High-Resolution Chest X-Ray Bone Suppression Using Unpaired CT Structural Priors

Han Li; Hu Han; Zeju Li; Lei Wang; Zhe Wu; Jingjing Lu; S. Kevin Zhou

IEEE Transactions on Medical Imaging 2020. [PDF]

Manual Suppression

Segmentation of Anatomical Structures on Chest Radiographs

S. Juhász; Á. Horváth; L. Nikházy; G. Horváth & Á. Horváth

XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010. [PDF]

By the way, BSE-JSRT dataset was just created by using this method!

GAN

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

Bo Zhou; Xunyu Lin; Brendan Eck; Jun Hou; David L. Wilson

ACCV 2018. [PDF] [Github]
⭐Learning Bone Suppression from Dual Energy Chest X-rays using Adversarial Networks

Dong Yul Oh; Il Dong Yun

arxiv 2018. [PDF]
Dilated conditional GAN for bone suppression in chest radiographs with enforced semantic features

Zhizhen Zhou; Luping Zhou; Kaikai Shen

Medical Physics 2020. [PDF] [Dataset for JSRT bone suppression]
⭐Generating Dual-Energy Subtraction Soft-Tissue Images from Chest Radiographs via Bone Edge-Guided GAN

Yunbi Liu; Mingxia Liu; Yuhua Xi; Genggeng Qin; Dinggang Shen; Wei Yang

MICCAI 2020. [PDF]
Bone Suppression on Chest Radiographs With Adversarial Learning

Jia Liang; Yuxing Tang; Youbao Tang; Jing Xiao; Ronald M. Summers

Medical Imaging 2020. [PDF] [Dataset for RSNA]
⭐Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

Mohammad Eslami; Solale Tabarestani; Shadi Albarqouni; Ehsan Adeli; Nassir Navab; Malek Adjouadi

IEEE Transactions on Medical Imaging 2020. [PDF] [Github] [Dataset for JSRT bone suppression] [Dataset for JSRT bone masks]
⭐Spatial feature and resolution maximization GAN for bone suppression in chest radiographs

Geeta Rani; Ankit Misra; Vijaypal Singh Dhaka; Ester Zumpano; Eugenio Vocaturo

Computer Methods and Programs in Biomedicine 2022. [PDF] [Dataset for JSRT bone suppression]
GAN-based disentanglement learning for chest X-ray rib suppression

Luyi Han; Yuanyuan Lyu; Cheng Peng; S. Kevin Zhou

Medical Image Analysis 2022. [PDF] [Dataset for LIDC-IDRI]
Applying a Conditional GAN for Bone Suppression in Chest Radiography Images

Hugo Eduardo Ziviani; Guillermo C´amara Ch´avez; Mateus Coelho Silva

Anais do XLIX Seminário Integrado de Software e Hardware 2022. [PDF]
Cycle-generative adversarial network-based bone suppression imaging for highly accurate markerless motion tracking of lung tumors for cyberknife irradiation therapy

Zennosuke Mochizuki, Masahide Saito, Toshihiro Suzuki, Koji Mochizuki, Junichi Hasegawa, Hikaru Nemoto, Kenichiro Satani, Hiroshi Takahashi, Hiroshi Onishi

Journal of Applied Clinical Medical Physics 2023. [PDF]
CycleGAN Based Bone Suppression Techniques for Standard CXR Images

V.Santhosh Kumar Tangudu, Jagadeesh Kakarla, A.Sam Prabhu & Bala Vikranth Kumar

Pattern Recognition 2024. [PDF]

Convolutional Neural Network

Bone suppression on pediatric chest radiographs via a deep learning-based cascade model

Kyungjin Cho; Jiyeon Seo; Sunggu Kyung; Mingyu Kim; Gil-Sun Hong; Namkug Kim

Computer Methods and Programs in Biomedicine 2022. [PDF]
⭐Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain

Wei Yang; Yingyin Chen; Yunbi Liu; Liming Zhong; Genggeng Qin; Zhentai Lu;Qianjin Feng; Wufan Chen

Medical Image Analysis 2017. [PDF]
⭐Deep Learning Models for Bone Suppression in Chest Radiographs

Maxim Gusarev; Ramil Kuleev; Adil Khan; Adin Ramirez Rivera; Asad Masood Khattak

CIBCB 2017. [PDF] [Github] [Dataset for bone suppression]
Bone Suppression of Chest Radiographs With Cascaded Convolutional Networks in Wavelet Domain

Yingyin Chen; Xiaofang Gou; Xiuxia Feng; Yunbi Liu; Genggeng Qin; Qianjin Feng; Wei Yang; Wufan Chen

IEEE ACCESS 2019. [PDF]
Separation of bones from soft tissue in chest radiographs: Anatomy-specific orientation-frequency-specific deep neural network convolution

Amin Zarshenas; Junchi Liu; Paul Forti; Kenji Suzuki

Medical Physics 2020. [PDF]
Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Data

Ophir Gozes; Hayit Greenspan

ISBI 2020. [PDF] [Dataset for LIDC-IDRI] [Dataset for NIH X-Ray14]
⭐Bone suppression for chest X-ray image using a convolutional neural filter

Naoki Matsubara; Atsushi Teramoto, Kuniaki Saito; Hiroshi Fujita

Physical and Engineering Sciences in Medicine 2020. [PDF]
⭐Improving Tuberculosis Recognition on Bone-Suppressed Chest X-rays Guided by Task-Specific Features

Yunbi Liu; Genggeng Qin; Yun Liu; Mingxia Liu; Wei Yang

PRIME 2021. [PDF]
Isometric Convolutional Neural Networks for Bone Suppression of Multi-Planar Dual Energy Chest Radiograph

Yossathorn Tianrungroj; Iba Hitoshi

IIAIAAI 2022. [PDF]
Deep learning-based bone suppression in chest radiographs using CT-derived features: a feasibility study

Ge Ren; Haonan Xiao; Sai-Kit Lam; Dongrong Yang; Tian Li; Xinzhi Teng; Jing Qin; Jing Cai

Quantitative Imaging in Medicine and Surgery 2021. [PDF]
Chest tomosynthesis image enhancement by bone suppression using convolutional neural networks with synthetic data

Xiaotong Xu, Qian Li, Shuang Jin, Zhe Su, Yu Zhang

Journal of Radiation Research and Applied Sciences 2024. [PDF]
xU-NetFullSharp: The Novel Deep Learning Architecture for Chest X-ray Bone Shadow Suppression

Vojtech Schiller, Radim Burgeta, Samuel Genzor, Jan Mizera, Anzhelika Mezina

Biomedical Signal Processing and Control 2025. [PDF]

Bone Suppression Application

Dual energy subtraction: Principles and clinical applications

Peter Vock; Zsolt Szucs-Farkas

European Journal of Radiology 2009. [PDF]
When Does Bone Suppression And Lung Field Segmentation Improve Chest X-Ray Disease Classification?

Ivo M. Baltruschat; Leonhard Steinmeister; Harald Ittrich; Gerhard Adam; Hannes Nickisch; Axel Saalbach; Jens von Berg; Michael Grass; Tobias Knopp

ISBI 2019. [PDF]
⭐Evaluation of Deep Learning Method for Bone Suppression from Dual Energy Chest Radiography

Ilyas Sirazitdinov; Konstantin Kubrak; Semen Kiselev; Alexey Tolkachev; Maksym Kholiavchenko; Bulat Ibragimov

ICANN 2020. [PDF]
⭐Chest X-ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings

Sivaramakrishnan Rajaraman; Ghada Zamzmi; Les Folio;; Philip Alderson; Sameer Antani

Diagnostics 2021. [PDF] [Github] [Dataset for Montgomery TB CXR] [Dataset for Shenzhen TB CXR][Dataset for RSNA CXR] [Dataset for Pediatric pneumonia CXR] [Dataset for JSRT bone suppression]
Value of bone suppression software in chest radiographs for improving image quality and reducing radiation dose

Gil-Sun Hong; Kyung-Hyun Do; A-Yeon Son; Kyung-Wook Jo; Kwang Pyo Kim; Jihye Yun; Choong Wook Lee

European Radiology 2021. [PDF]
Bone Suppression on Chest Radiographs for Pulmonary Nodule Detection: Comparison between a Generative Adversarial Network and Dual-Energy Subtraction

Kyungsoo Bae; Dong Yul Oh; Il Dong Yun; and Kyung Nyeo Jeon

Korean Journal of Radiology 2022. [PDF]
Improved detection of solitary pulmonary nodules on radiographs compared with deep bone suppression imaging

Jiefang Wu; Weiguo Chen; Fengxia Zeng; Le Ma; Weimin Xu; Wei Yang; Genggeng Qin

Quantitative Imaging in Medicine and Surgery 2021. [PDF]
⭐DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs

Sivaramakrishnan Rajaraman; Gregg Cohen; Lillian Spear; Les Folio; Sameer Antani

PLOS ONE 2022. [PDF] [Github]
Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs

Ngo Fung Daniel Lam; Hongfei Sun; Liming Song; Dongrong Yang; Shaohua Zhi; Ge Ren; Pak Hei Chou; Shiu Bun Nelson Wan; Man Fung Esther Wong; King Kwong Chan; Hoi Ching Hailey Tsang; Feng-Ming (Spring) Kong; Yì Xiáng J. Wáng; Jing Qin; Lawrence Wing Chi Chan; Michael Ying; Jing Cai

Quantitative Imaging in Medicine and Surgery 2022. [PDF] [Dataset for JSRT bone suppression]
Development and Validation of a Deep Learning–Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs

Hwiyoung Kim; Kye Ho Lee; Kyunghwa Han, PhD; Ji Won Lee; Jin Young Kim; Dong Jin Im; Yoo Jin Hong; Byoung Wook Choi; Jin Hur, MD, PhD

JAMA Network Open 2023. [PDF]
BSD: A multi-task framework for pulmonary disease classification using deep learning

Sanli Yi, Shenglin Qin, Furong She, Dangguo Shao

Expert Systems With Applications 2025. [PDF]

Datasets

Dual Energy Subtraction(DES)

Dataset	Resolution	Class	Collected	Private or Public	#	Link
JSRT/BSE-JSRT	2048 × 2048	Nodule/No Nodule	Japanese Society of Radiological Technology	Public	247/240	https://www.kaggle.com/hmchuong/xray-bone-shadow-supression
Gusarev M et.al	Random	-----	Different online	Public	35	https://drive.google.com/drive/folders/1VLD9deplqACJpdd47EdZCVW2BIN4eM95?usp=sharing
Yunbi Liu et.al	----	----	Nanfang Hospital, China	Private	646/504	----

For 240 paired JSRT original and bone suppressed images, you can also visit this [website].

CT

Dataset	Resolution	Classes	Collected	Private or Oublic	#	Link
LIDC-IDRI	Random	nodule >or =3mm,nodule < 3mm,non-nodule >or = 3mm	The Cancer Imaging Archive(TCIA)	Public	1018	https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=1966254
RIDER	Random	Cancer	The Cancer Imaging Archive(TCIA)	Public	154	https://wiki.cancerimagingarchive.net/display/public/rider+lung+ct

CXR

Dataset	Resolution	Classes	Collected	#	Link
Indiana	512 × 512	Multiple diseases including opacity, cardiomegaly, pleural effusion, and pulmonary edema	Indiana Network for Patient Care with various hospitals associated with the Indiana University School of Medicine	7470	https://www.kaggle.com/datasets/raddar/chest-xrays-indiana-university
ChestX-ray8	1024 × 1024	8 findings including pneumonia, atelectasis, mass, pneumothorax, infiltration, cardiomegaly, effusion, and nodule	From clinical PACS databases in the hospitals associated to NIHCC (National Institutes of Health Clinical Center)	108,948	https://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.pdf
ChestX-ray14	1024 × 1024	14 findings including hernia, consolidation, emphysema edema, pleural thickening, pulmonary fibrosis, and others	From clinical PACS databases in the hospitals associated to NIHCC (National Institutes of Health Clinical Center)	112,120	https://www.v7labs.com/open-datasets/chestx-ray14
Montgomery	4020 × 4892	Normal and TB	Montgomery County Department of Health and Human Services	138	https://www.kaggle.com/datasets/raddar/tuberculosis-chest-xrays-montgomery
Shenzhen	3000 × 3000	Normal and TB	In collaboration with Shenzhen No. 3 People’s Hospital, Guangdong Medical College, Shenzhen, China	662	https://www.kaggle.com/datasets/raddar/tuberculosis-chest-xrays-shenzhen
RSNA-Pneumonia-CXR	Random	Pneumonia, infiltration, and consolidation	The RSNA (Radiological Society of North America) and the STR (Society of Thoracic Radiology)	15,000	https://www.kaggle.com/competitions/rsna-pneumonia-detection-challenge/data
Pediatric-CXR	Random	Normal, bacterial-pneumonia, viral-pneumonia	Guangzhou Women and Children’s Medical Center, China	5856	https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
Padchest	Random	Large number of findings	San Juan Hospital (Spain)	160,868	https://bimcv.cipf.es/bimcv-projects/padchest/
CheXpert	Random	14 findings including edema, cardiomegaly, lung opacity, lung lesion, consolidation, pneumonia, atelectasis, pneumothorax, and others	Stanford University Medical Center	224,316	https://stanfordmlgroup.github.io/competitions/chexpert/
TBX11K	512 × 512	5 findings including Healthy, Sick but Non-TB, Active TB, Latent TB, and Uncertain TB	Paper: Rethinking Computer-aided Tuberculosis Diagnosis	11,200	https://www.kaggle.com/datasets/usmanshams/tbx-11
MIMIC-CXR	2544 × 3056	14 diseases (227,943 imaging studies)	MIT, Beth Israel Deaconess Medical Center (Boston, MA, USA)	473,057	https://www.v7labs.com/open-datasets/mimic-cxr
VinDr-CXR	Random	28 findings including TB, pneumonia, cardiomegaly, pleural effusion, lung opacity and others	The Hospital 108 (H108) and the HMUH (Hanoi Medical University Hospital)	18,000	https://vindr.ai/datasets/cxr

CXR(COVID)

Dataset	Resolution	Classes	Collected	#	Link
COVIDx CXR-3	Random	Positive and negative COVID-19	Paper: COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics	30,386	https://www.kaggle.com/datasets/andyczhao/covidx-cxr2
COVIDx CXR-4	Random	Positive and negative COVID-19, Non-COVID infections	Paper: COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images	84,818	https://github.com/lindawangg/COVID-Net/blob/master/docs/COVIDx.md
COVID-QU-Ex	256 X 256	COVID-19, Non-COVID infections (Viral or Bacterial Pneumonia) and Normal	Qatar University	33920	https://www.kaggle.com/datasets/anasmohammedtahir/covidqu/
COVID-19 Radiography Database	299 X 299	COVID-19 positive cases, Non-COVID infections, Normal and Viral Pneumonia	Qatar University and the University of Dhaka	3616	https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database
COVID-19-AR	Unclear	Unclear	The University of Arkansas for Medical Sciences (UAMS) Translational Research Institute	31935	https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226443#702264439f6e465db7e5421b8f47e08415e28227

Obviously, there must have other datsets which are not displayed in these three tables. Let us know and we'll add them to this list if you find them.

By the way, T. Weber et al. provides an API called MaCheX which is a composition of mutliple public chest radiography datasets.

Mask

Dataset	Resolution	#	Link
JSRT Mask	2048 × 2048	240	https://www.kaggle.com/datasets/yoctoman/jsrt-original-and-bone-masks?resource=download

Acknowledges

Thanks to Junjie Wang for providing us with more public CXR datasets!

Some Additional Resources