Lung Texture

Lung texture analysis focuses on identifying and characterizing patterns in lung tissue density and appearance from medical images, primarily to aid in the diagnosis and monitoring of various pulmonary diseases. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs) and vision transformers, often incorporating techniques like transfer learning and self-supervised learning to improve accuracy and efficiency in tasks such as disease classification and lesion segmentation. These advancements are significantly impacting clinical practice by enabling faster, more objective, and potentially more accurate diagnosis of lung diseases, including COVID-19 and interstitial lung diseases, ultimately improving patient care and management.

Papers

November 5, 2024

Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation
Zhiling Yue, Yingying Fang, Liutao Yang, Nikhil Baid, Simon Walsh, Guang Yang
Semantic Segmentation Image Generation Supervised Semantic Segmentation Lung Texture Idiopathic Pulmonary Fibrosis

October 26, 2024

Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentation
Jiasen Zhang, Mingrui Yang, Weihong Guo, Brian A. Xavier, Michael Bolen, Xiaojuan Li
Transfer Learning Full Model Lung Cancer Lung Nodule Target Modality Lung Texture Lung Nodule Segmentation Spatially Varying Regularization

March 1, 2024

Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies
Artur Wysoczanski, Nabil Ettehadi, Soroush Arabshahi, Yifei Sun, Karen Hinkley Stukovsky, Karol E. Watson, MeiLan K. Han, Erin D Michos, Alejandro P. Comellas, Eric A. Hoffman, Andrew F. Laine, R. Graham Barr, Elsa D. Angelini
Convolutional Neural Network Robust Deep Chest CT Chronic Obstructive Pulmonary Disease Squeeze Flow Mesa Optimization Lung Texture

November 28, 2023

Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture
Md. Alamin Talukder, Md. Abu Layek, Mohsin Kazi, Md Ashraf Uddin, Sunil Aryal
System Performance COVID 19 Detection Efficient Deep COVID 19 Diagnosis Chest X Ray Datasets Lung Texture COVID 19 Chest X Ray

September 18, 2023

BRONCO: Automated modelling of the bronchovascular bundle using the Computed Tomography Images
Wojciech Prażuch, Marek Socha, Anna Mrukwa, Aleksandra Suwalska, Agata Durawa, Malgorzata Jelitto-Górska, Katarzyna Dziadziuszko, Edyta Szurowska, Pawel Bożek, Michal Marczyk, Witold Rzyman, Joanna Polanska
Segmentation Based Approach CT Image Airway Tree Model Generation Segmentation Pipeline Lung Texture Arterial Blood Pressure

September 10, 2023

Lung Diseases Image Segmentation using Faster R-CNNs
Mihir Jain
Loss Function Deep Network Lung Segmentation Faster R CNN Dense Neural Network Non Maximum Suppression Lung Texture

July 29, 2023

CoVid-19 Detection leveraging Vision Transformers and Explainable AI
Pangoth Santhosh Kumar, Kundrapu Supriya, Mallikharjuna Rao K, Taraka Satya Krishna Teja Malisetti
Vision Transformer Explainable AI COVID 19 Detection Epidemic Forecasting Lung Disease Lung Texture

March 20, 2023

Deep learning automated quantification of lung disease in pulmonary hypertension on CT pulmonary angiography: A preliminary clinical study with external validation
Michael J. Sharkey, Krit Dwivedi, Samer Alabed, Andrew J. Swift
Lung Disease Pulmonary Angiography Pulmonary Hypertension Lung Texture Lung Region

November 1, 2022

RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images
Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama
Medical Image Latent Representation Masked Image Modeling X Ray Image Lung Texture

June 14, 2022

CNN-based Classification Framework for Lung Tissues with Auxiliary Information
Huafeng Hu, Ruijie Ye, Jeyarajan Thiyagalingam, Frans Coenen, Jionglong Su
Auxiliary Information Lung Texture Idiopathic Pulmonary Fibrosis Convolutional Neural Network Based Framework

February 24, 2022

A novel unsupervised covid lung lesion segmentation based on the lung tissue identification
Faeze Gholamian Khah, Samaneh Mostafapour, Seyedjafar Shojaerazavi, Nouraddin Abdi-Goushbolagh, Hossein Arabi
Covid 19 Long Form Novel Lung Segmentation Lung Texture Infection Segmentation

January 18, 2022

Lung Swapping Autoencoder: Learning a Disentangled Structure-texture Representation of Chest Radiographs
Lei Zhou, Joseph Bae, Huidong Liu, Gagandeep Singh, Jeremy Green, Amit Gupta, Dimitris Samaras, Prateek Prasanna
Chest Radiograph Prediction Performance Texture Representation Disentangled Haze Lung Texture

Lung Texture

Papers

Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation

Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentation

Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies

Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture

BRONCO: Automated modelling of the bronchovascular bundle using the Computed Tomography Images

Lung Diseases Image Segmentation using Faster R-CNNs

CoVid-19 Detection leveraging Vision Transformers and Explainable AI

Deep learning automated quantification of lung disease in pulmonary hypertension on CT pulmonary angiography: A preliminary clinical study with external validation

RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images

CNN-based Classification Framework for Lung Tissues with Auxiliary Information

A novel unsupervised covid lung lesion segmentation based on the lung tissue identification

Lung Swapping Autoencoder: Learning a Disentangled Structure-texture Representation of Chest Radiographs