Development and Evaluation of a Deep Learning Algorithm for Rib                     Segmentation and Fracture Detection from Multicenter Chest CT                     Images

Wu, Mingxiang; Chai, Zhizhong; Qian, Guangwu; Lin, Huangjing; Wang, Qiong; Wang, Liansheng; Chen, Hao

doi:10.1148/ryai.2021200248

Cited by 27 publications

(21 citation statements)

References 25 publications

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“…The segmentation image size of 512×512 pixel [ 157 , 158 ]. In [ 159 ], the authors used this model for segmentation of thoracic fracture disease, and in [ 100 ], the authors used U-Net in segmentation of cardiomegaly disease.…”

Section: The Taxonomy Of State-of-the-art Work On Thoracic Diseases D...mentioning

confidence: 99%

“…Heart diseases include cardiomegaly [ 128 ] and heart insufficiency disease [ 178 ]. Other diseases include fracture [ 159 ], hernia [ 179 ], and mass [ 180 ].…”

Section: The Taxonomy Of State-of-the-art Work On Thoracic Diseases D...mentioning

confidence: 99%

“… Ref. (Year) Name of Disease Input Image Type Dataset Used Data Preparation Type Model Type Ensemble Technique Target Results Open Issues [ 159 ] (2021) Fracture CT Private R-CNN for classification & UNet for Segmentation weighted average of probabilities Segmentation and detection Average Sensitivity 89.2% and Precision 88.4% A 3D-CNN model can be used as a classification model to further classify the observed rib fracture types from the existing model. The performance of the rib segmentation and labelling algorithm must be improved.…”

Section: The Taxonomy Of State-of-the-art Work On Thoracic Diseases D...mentioning

confidence: 99%

“…Wu et al [ 159 ] used a three-dimensional rib segmentation model (U-Net) and a deep learning R-CNN pre-trained on the ResNet50 algorithm capable of recognizing rib fractures and related anatomic locations on CT images. First, they scanned the rib fractures and the ribs segmented section by section using a two-dimensional (2D) detection network.…”

Section: The Taxonomy Of State-of-the-art Work On Thoracic Diseases D...mentioning

confidence: 99%

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A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images

et al. 2022

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Thoracic diseases refer to disorders that affect the lungs, heart, and other parts of the rib cage, such as pneumonia, novel coronavirus disease (COVID-19), tuberculosis, cardiomegaly, and fracture. Millions of people die every year from thoracic diseases. Therefore, early detection of these diseases is essential and can save many lives. Earlier, only highly experienced radiologists examined thoracic diseases, but recent developments in image processing and deep learning techniques are opening the door for the automated detection of these diseases. In this paper, we present a comprehensive review including: types of thoracic diseases; examination types of thoracic images; image pre-processing; models of deep learning applied to the detection of thoracic diseases (e.g., pneumonia, COVID-19, edema, fibrosis, tuberculosis, chronic obstructive pulmonary disease (COPD), and lung cancer); transfer learning background knowledge; ensemble learning; and future initiatives for improving the efficacy of deep learning models in applications that detect thoracic diseases. Through this survey paper, researchers may be able to gain an overall and systematic knowledge of deep learning applications in medical thoracic images. The review investigates a performance comparison of various models and a comparison of various datasets.

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Section: The Taxonomy Of State-of-the-art Work On Thoracic Diseases D...mentioning

confidence: 99%

“…Heart diseases include cardiomegaly [ 128 ] and heart insufficiency disease [ 178 ]. Other diseases include fracture [ 159 ], hernia [ 179 ], and mass [ 180 ].…”

Section: The Taxonomy Of State-of-the-art Work On Thoracic Diseases D...mentioning

confidence: 99%

Section: The Taxonomy Of State-of-the-art Work On Thoracic Diseases D...mentioning

confidence: 99%

Section: The Taxonomy Of State-of-the-art Work On Thoracic Diseases D...mentioning

confidence: 99%

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A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images

et al. 2022

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“…Previously, several studies on the segmentation of ribs and labeling their sequence have been published [11][12][13]. Staal et al [14] published a labeling system by multiple deep learning networks on a 3D reconstructed model using chest CT.…”

Section: Introductionmentioning

confidence: 99%

The Development of an Automatic Rib Sequence Labeling System on Axial Computed Tomography Images with 3-Dimensional Region Growing

Seol

Kim

Park

et al. 2022

Sensors

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This paper proposes a development of automatic rib sequence labeling systems on chest computed tomography (CT) images with two suggested methods and three-dimensional (3D) region growing. In clinical practice, radiologists usually define anatomical terms of location depending on the rib’s number. Thus, with the manual process of labeling 12 pairs of ribs and counting their sequence, it is necessary to refer to the annotations every time the radiologists read chest CT. However, the process is tedious, repetitive, and time-consuming as the demand for chest CT-based medical readings has increased. To handle the task efficiently, we proposed an automatic rib sequence labeling system and implemented comparison analysis on two methods. With 50 collected chest CT images, we implemented intensity-based image processing (IIP) and a convolutional neural network (CNN) for rib segmentation on this system. Additionally, three-dimensional (3D) region growing was used to classify each rib’s label and put in a sequence label. The IIP-based method reported a 92.0% and the CNN-based method reported a 98.0% success rate, which is the rate of labeling appropriate rib sequences over whole pairs (1st to 12th) for all slices. We hope for the applicability thereof in clinical diagnostic environments by this method-efficient automatic rib sequence labeling system.

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A survey on artificial intelligence in pulmonary imaging

Saha

Nadeem

Comellas

2023

WIREs Data Min & Knowl

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Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID‐19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This article presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL‐based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.This article is categorized under: Application Areas > Health Care Technologies > Artificial Intelligence Technologies > Computational Intelligence Application Areas > Science and Technology

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Development and Evaluation of a Deep Learning Algorithm for Rib Segmentation and Fracture Detection from Multicenter Chest CT Images

Cited by 27 publications

References 25 publications

A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images

A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images

The Development of an Automatic Rib Sequence Labeling System on Axial Computed Tomography Images with 3-Dimensional Region Growing

A survey on artificial intelligence in pulmonary imaging

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