Application of CAD systems for the automatic detection of lung nodules

Shariaty, Faridoddin; Mousavi, Mojtaba

doi:10.1016/j.imu.2019.100173

Cited by 47 publications

(18 citation statements)

References 28 publications

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“…Coordinates of the raw medical image data are in the world coordinate system. We need to convert the world coordinate system to the image coordinate system as illustrated in (4). image_coordinates = |world_coordinates − origin| spacing (4) Fig.…”

Section: ) Coordinate Transformationmentioning

confidence: 99%

“…In the process of lung cancer screening, radiologists are prone to make mistakes due to a large number of slices. Hence, it is necessary to develop a Computer-Aided Diagnosis (CAD) system that can not only automatically detect but also assist to observe the pulmonary nodules [4]. Traditional auxiliary diagnosis system for pulmonary nodules includes preprocessing, lung segmentation, Region of Interest (RoI) detection, feature extraction, feature selection, classification, false positive reduction and other steps [5].…”

Section: Introductionmentioning

confidence: 99%

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Mask R-CNN-Based Detection and Segmentation for Pulmonary Nodule 3D Visualization Diagnosis

et al. 2020

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3D visualization diagnosis for pulmonary nodule detection and segmentation is becoming a promising topic in the field of surgical researches and applications. Aiming at assisting radiologists to diagnose pulmonary nodules more accurately, the methods of detection and segmentation for pulmonary nodule 3D visualization diagnosis were proposed based on Mask Region-Convolutional Neural Network (Mask R-CNN) and ray-casting volume rendering algorithm. The Mask R-CNN used resnet50 as the backbone and applied Feature Pyramid Network (FPN) to fully explore multiscale feature maps. And then, Region Proposal Network (RPN) was used to propose candidate bounding boxes. Furthermore, the mask matrices and the raw medical image sequences were multiplied to obtain sequences of predicted pulmonary nodules. Finally, ray-casting volume rendering algorithm was applied to generate the 3D models of pulmonary nodules. The proposed methods are tested and evaluated on publicly available LUNA16 dataset and the independent dataset from Ali TianChi challenge. Experimental results show that Mask R-CNN of weighted loss reaches sensitivities of 88.1% and 88.7% at 1 and 4 false positives per scan, respectively. Meanwhile, we can obtain AP@50 score of 0.882 using Mask R-CNN with weighted loss on labelme_LUNA16 dataset, which outperforms many existing state-of-the-art approaches of detection and segmentation of pulmonary nodules.

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Section: ) Coordinate Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mask R-CNN-Based Detection and Segmentation for Pulmonary Nodule 3D Visualization Diagnosis

et al. 2020

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“…For each patient, thousands of images are acquired, and it is extremely hard for a single physician to analyze all of them in detail [ 63 ]. Furthermore, human analysis is limited because of its subjective nature, in particular when comparing nodules pattern [ 64 ]. Conversely, computers/artificial intelligence can accomplish a specific procedure with high precision, often in a shorter time.…”

Section: Imaging Evaluationmentioning

confidence: 99%

Radiologic Assessment of Osteosarcoma Lung Metastases: State of the Art and Recent Advances

Chiesa

Spinnato

Miceli

et al. 2021

Cells

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The lung is the most frequent site of osteosarcoma (OS) metastases, which are a critical point in defining a patient’s prognosis. Chest computed tomography (CT) represents the gold standard for the detection of lung metastases even if its sensitivity widely ranges in the literature since lung localizations are often atypical. ESMO guidelines represent one of the major references for the follow-up program of OS patients. The development of new reconstruction techniques, such as the iterative method and the deep learning-based image reconstruction (DLIR), has led to a significant reduction of the radiation dose with the low-dose CT. The improvement of these techniques has great importance considering the young-onset of the disease and the strict chest surveillance during follow-up programs. The use of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT is still controversial, while volume doubling time (VDT) and computer-aided diagnosis (CAD) systems are recent diagnostic tools that could support radiologists for lung nodules evaluation. Their use, well-established for other malignancies, needs to be further evaluated, focusing on OS patients.

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“…CT images are generally used for processing medical images, because of their low noise level and high resolution. The use of Computer-Aided identification system in lung cancer treatment is studied in this paper, consisting segmentation as well as pre-processing techniques, as well as data analysis methods [10]. In order to help with collection, analysis, evaluation of imagery medical information, the main objective was to discover new technology for computational diagnostic tools development.…”

Section: Introductionmentioning

confidence: 99%

Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach

Bhandary¹,

G²,

Basthikodi³

et al. 2021

IJETT

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Lung cancer begins in the lungs and leading to the reason of cancer demise amid population in the creation. According to the American Cancer Society, which estimates about 27% of the deaths because of cancer. In the early phase of its evolution, lung cancer does not cause any symptoms usually. Many of the patients have been diagnosed in a developed phase where symptoms become more prominent, that results in poor curative treatment and high mortality rate. Computer Aided Detection systems are used to achieve greater accuracies for the lung cancer diagnosis. In this research exertion, we proposed a novel methodology for lung Segmentation on the basis of Fuzzy C-Means Clustering, Adaptive Thresholding, and Segmentation of Active Contour Model. The experimental results are analysed and presented.

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Application of CAD systems for the automatic detection of lung nodules

Cited by 47 publications

References 28 publications

Mask R-CNN-Based Detection and Segmentation for Pulmonary Nodule 3D Visualization Diagnosis

Mask R-CNN-Based Detection and Segmentation for Pulmonary Nodule 3D Visualization Diagnosis

Radiologic Assessment of Osteosarcoma Lung Metastases: State of the Art and Recent Advances

Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach

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