Deep Learning–based Approach for Automated Assessment of                     Interstitial Lung Disease in Systemic Sclerosis on CT Images

Chassagnon, Guillaume; Vakalopoulou, Maria; Régent, Alexis; Zacharaki, Evangelia I.; Aviram, Galit; Martin, Charlotte; Marini, Rafael; Bus, Norbert; Jerjir, Naim; Mékinian, A.; Hua-Huy, T.; Monnier‐Cholley, Laurence; Benmostefa, Nouria; Mouthon, Luc; Dinh-Xuan, Anh-Tuan; Paragios, Nikos; Revel, Marie‐Pierre

doi:10.1148/ryai.2020190006

Cited by 45 publications

(19 citation statements)

References 36 publications

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“…Several categories of DL have been developed, including fully convolutional networks [ 15 ], encoder–decoder networks [ 16 ], multi-scale and pyramid network-based models [ 17 , 18 , 19 ], attention-based models [ 20 ], recurrent neural network-based models [ 21 ], and those based on generative and adversarial training [ 22 , 23 ]. For the lung segmentation task, several deep learning-based methods have been proposed (e.g., [ 6 , 11 , 24 , 25 , 26 ]). The interested reader is encouraged to refer to comprehensive reviews [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies

et al. 2020

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Background: The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model. Methods: Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources’ requirements. Results: E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region. Conclusions: We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results.

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Section: Introductionmentioning

confidence: 99%

Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies

et al. 2020

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“…DL has been used to detect and classify diseases such as lung cancer and pulmonary nodules from CT imaging [60], neurodegenerative diseases [61], Alzheimer's disease from fMRI and MRI scans [6], eye-related disorders [15], and detection of skin diseases from photographs [62]. These studies are dependent on large volumes of training data to achieve better DL classification performance.…”

Section: Methodsmentioning

confidence: 99%

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review

Wassan

Zheng

Wang

2021

Cells

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Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highly prevalent and could impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been used to predict aging-related diseases and issues, aiding clinical providers in decision-making based on patient’s medical records. Deep learning (DL), as one of the most recent generations of AI technologies, has embraced rapid progress in the early prediction and classification of aging-related issues. In this paper, a scoping review of publications using DL approaches to predict common aging-related diseases (such as age-related macular degeneration, cardiovascular and respiratory diseases, arthritis, Alzheimer’s and lifestyle patterns related to disease progression), was performed. Google Scholar, IEEE and PubMed are used to search DL papers on common aging-related issues published between January 2017 and August 2021. These papers were reviewed, evaluated, and the findings were summarized. Overall, 34 studies met the inclusion criteria. These studies indicate that DL could help clinicians in diagnosing disease at its early stages by mapping diagnostic predictions into observable clinical presentations; and achieving high predictive performance (e.g., more than 90% accurate predictions of diseases in aging).

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“…Advancements within the past ten years have led to a new division within machine learning. Deep learning (DL) is a learning algorithm designed to mimic the function of human brain in the form of neural networks [15]. An advanced subsection of ML, DL is able to perform artificial intelligence functions with extensive training resources.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative Study

Morgan

Wang

et al. 2022

IJGI

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Deep learning techniques are increasingly being recognized as effective image classifiers. Aside from their successful performance in past studies, the accuracies have varied in complex environments, in comparison with the popularly of applied machine learning classifiers. This study seeks to explore the feasibility of using a U-Net deep learning architecture to classify bi-temporal, high-resolution, county-scale aerial images to determine the spatial extent and changes of land cover classes that directly or indirectly impact tidal marsh. The image set used in the analysis is a collection of a 1-m resolution collection of National Agriculture Imagery Program (NAIP) tiles from 2009 and 2019, covering Beaufort County, South Carolina. The U-Net CNN classification results were compared with two machine learning classifiers, the random trees (RT) and support vector machine (SVM). The results revealed a significant accuracy advantage in using the U-Net classifier (92.4%), as opposed to the SVM (81.6%) and RT (75.7%) classifiers, for overall accuracy. From the perspective of a GIS analyst or coastal manager, the U-Net classifier is now an easily accessible and powerful tool for mapping large areas. Change detection analysis indicated little areal change on marsh extent, though increased land development throughout the county has the potential to negatively impact the health of the marshes. Future work should explore applying the constructed U-Net classifier to coastal environments in large geographic areas, while also implementing other data sources (e.g., LIDAR and multispectral data) to enhance classification accuracy.

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Deep Learning–based Approach for Automated Assessment of Interstitial Lung Disease in Systemic Sclerosis on CT Images

Cited by 45 publications

References 36 publications

Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies

Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review

Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative Study

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