Segmentation of Lung Nodules in CT Scan Data

2015 ◽  
Vol 3 (2) ◽  
pp. 66-77 ◽  
Author(s):  
Shehzad Khalid ◽  
Anwar C. Shaukat ◽  
Amina Jameel ◽  
Imran Fareed
Keyword(s):  
Lung Cancer ◽  
Lung Nodule ◽  
Clinical Importance ◽  
Pulmonary Nodules ◽  
Cad System ◽  
Nodule Detection ◽  
Scan Data ◽  
Effective Domain ◽  
Aided Diagnosis

Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. Several studies have shown the feasibility and robustness of automated matching of corresponding nodule pairs between follow up examinations. Different image pre-processing and segmentation techniques are used in various research sides to segment different tumors or ulcers from different images. This paper aims to make a review on the existing segmentation algorithms used for CT images of pulmonary nodules and presents a study of the existing methods on automated lung nodule detection. It provides a comparison of the performance of the existing approaches in regards to effective domain results.

Segmentation of Lung Nodules in CT Scan Data

2017 ◽  
pp. 601-613 ◽  
Author(s):  
Shehzad Khalid ◽  
Anwar C. Shaukat ◽  
Amina Jameel ◽  
Imran Fareed
Keyword(s):  
Lung Cancer ◽  
Lung Nodule ◽  
Clinical Importance ◽  
Pulmonary Nodules ◽  
Cad System ◽  
Nodule Detection ◽  
Scan Data ◽  
Effective Domain ◽  
Aided Diagnosis

Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. Several studies have shown the feasibility and robustness of automated matching of corresponding nodule pairs between follow up examinations. Different image pre-processing and segmentation techniques are used in various research sides to segment different tumors or ulcers from different images. This paper aims to make a review on the existing segmentation algorithms used for CT images of pulmonary nodules and presents a study of the existing methods on automated lung nodule detection. It provides a comparison of the performance of the existing approaches in regards to effective domain results.

scholarly journals Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinglun Liang ◽  
Guoliang Ye ◽  
Jianwen Guo ◽  
Qifan Huang ◽  
Shaohui Zhang
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
False Positives ◽  
Classification Systems ◽  
Cad System ◽  
Nodule Detection ◽  
Diagnosis Of Lung Cancer ◽  
Lung Ct

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

scholarly journals Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fukui Liang ◽  
Caiqin Li ◽  
Xiaoqin Fu
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Chest Ct ◽  
Detection Sensitivity ◽  
Lung Nodule Detection ◽  
Nodule Detection

Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient’s survival rate. Therefore, early diagnosis of lung cancer is very important. Early-stage lung cancer usually appears as a solitary lung nodule on medical imaging. It usually appears as a round or nearly round dense shadow in the chest radiograph. It is difficult to distinguish lung nodules and lung soft tissues with the naked eye. Therefore, this article proposes a deep learning-based artificial intelligence chest CT lung nodule detection performance evaluation study, aiming to evaluate the value of chest CT imaging technology in the detection of noncalcified nodules and provide help for the detection and treatment of lung cancer. In this article, the Lung Medical Imaging Database Consortium (LIDC) was selected to obtain 536 usable cases based on inclusion criteria; 80 cases were selected for examination, artificial intelligence software, radiologists, and thoracic imaging specialists. Using 80 pulmonary nodules detection in each case, the pathological type of pulmonary nodules, nonlime tuberculous test results, detection sensitivity, false negative rate, false positive rate, and CT findings were individually analyzed, and the detection efficiency software of artificial intelligence was evaluated. Experiments have proved that the sensitivity of artificial intelligence software to detect noncalcified nodules in the pleural, peripheral, central, and hilar areas is higher than that of radiologists, indicating that the method proposed in this article has achieved good detection results. It has a better nodule detection sensitivity than a radiologist, reducing the complexity of the detection process.

scholarly journals Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

2013 ◽  
Vol 2013 ◽  
pp. 1-46 ◽  
Author(s):  
Ayman El-Baz ◽  
Garth M. Beache ◽  
Georgy Gimel'farb ◽  
Kenji Suzuki ◽  
Kazunori Okada ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Clinical Importance ◽  
Computer Aided Diagnosis ◽  
Cad Systems ◽  
Cad System ◽  
Lung Cancer Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Processing Steps

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effectivecomputer-aided diagnosis(CAD) system for lung cancer is of great clinical importance and can increase the patient’s chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

scholarly journals Design of CAD System of Solitary Pulmonary Nodule with Harmony Classification and Fuzzy System

2017 ◽  
Vol 1 (4) ◽  
pp. 102-102
Author(s):  
Maryam Sadat Mahmoodi ◽  
Seyed Abbas Mahmoodi
Keyword(s):  
Lung Cancer ◽  
Fuzzy System ◽  
Search Algorithm ◽  
Harmony Search ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Harmony Search Algorithm ◽  
Cad System ◽  
Nodule Detection ◽  
Base Method

Introduction: Lung cancer is the most wide spread from of cancer, with the highest mortality rate worldwide. In this study, a computer-aided detection (CAD) system was developed for lung nodule detection, segmentation and recognition using CT images. So, we use a highly accurate supervised that uses lung images with the aim of assisting physicians in early detection of lung cancer. Methods: First, we segmented the lung area by masking techniques to isolated nodules and determined region of interest. Then, 24 features were extracted from images that included morphological, statistical and histogram. Important features  were derived from the images for their posterior analysis with the aid of a harmony search algorithm and fuzzy systems. Results: In order to evaluate the performance of the proposed method, we used the LIDC database. the number of images included a database of  97 images whom 47 were diagnosed with lung cancer. Results of the base method show a sensitivity of 93%. Conclusion: The harmony search algorithm is optimized using fuzzy system for classification. The CAD system provides 93.1%  accuracy.

Computer-aided lung cancer screening with CT: A clinically usable nodule detection and assessment system.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 7562-7562
Author(s):  
Pechin Lo ◽  
Matthew S. Brown ◽  
Jonathan Goldin ◽  
Eran Barnoy ◽  
Hyun J. Kim ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
False Positive ◽  
Lung Nodule ◽  
Lung Cancer Screening ◽  
Assessment System ◽  
Slice Thickness ◽  
Cad System ◽  
Nodule Detection ◽  
Computer Aided

7562 Background: The National Lung Screening Trial (NLST) recently demonstrated that lung cancer screening with low-dose CT reduces mortality. Current protocols use 4–8 mm nodules as positive screens. While there are some computer-aided nodule detection (CAD) systems currently available, they are rarely used in clinical practice because they generate too many false positives and lack reliable measurement tools. The purpose of this work is to develop a new CAD system to overcome these limitations and evaluate it against an expert panel of radiologists. Methods: The CAD system developed for lung nodule detection and measurement incorporates computer vision techniques including intensity thresholding, Euclidean Distance Transformation, and watershed segmentation. Rules pertaining to volume and shape were applied to automatically discriminate between nodules and bronchovascular anatomy. CAD system performance was assessed using 108 consecutive cases from the publically available Lung Imaging Database Consortium (LIDC), in which four radiologists reviewed each case. CT slice thickness ranged from 0.6–3.0 mm. Nodules were included that were: (a) ≥ 4mm, and (b) marked by a majority of the LIDC readers, and (c) ≥ 4 x CT slice thickness (to ensure adequate spatial resolution). Results: 44 of 108 subjects had one or more nodules meeting criteria. Median CAD sensitivity per subject for these 44 cases is reported for all nodules ≥ 4mm and the subset of nodules ≥ 8mm. The false positive (FP) rate per subject is reported for all 108 cases. The overall concordance correlation coefficient (CCC) between the CAD volume of each nodule and the LIDC reference volume was measured. Conclusions: Based on clinical CT screening protocols, a CAD system has been developed with high nodule sensitivity and a much lower false positive rate than previously reported systems. Automated volume measurements show strong agreement with the reference standard, providing a comprehensive detection and assessment workflow for lung cancer screening. [Table: see text]

scholarly journals Development of CAD System for Automatic Lung Nodule Detection: A Review

2021 ◽  
Vol 41 ◽  
pp. 04001
Author(s):  
Sekar Sari ◽  
Indah Soesanti ◽  
Noor Akhmad Setiawan
Keyword(s):  
Lung Cancer ◽  
Feature Extraction ◽  
Cancer Detection ◽  
False Positive ◽  
Lung Nodule ◽  
Cad Systems ◽  
Cad System ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Lung Cancer Detection

Lung cancer is a type of cancer that spreads rapidly and is the leading cause of mortality globally. The Computer-Aided Detection (CAD) system for automatic lung cancer detection has a significant influence on human survival. In this article, we report the summary of relevant literature on CAD systems for lung cancer detection. The CAD system includes preprocessing techniques, segmentation, lung nodule detection, and false-positive reduction with feature extraction. In evaluating some of the work on this topic, we used a search of selected literature, the dataset used for method validation, the number of cases, the image size, several techniques in nodule detection, feature extraction, sensitivity, and false-positive rates. The best performance CAD systems of our analysis results show the sensitivity value is high with low false positives and other parameters for lung nodule detection. Furthermore, it also uses a large dataset, so the further systems have improved accuracy and precision in detection. CNN is the best lung nodule detection method and need to develop, it is preferable because this method has witnessed various growth in recent years and has yielded impressive outcomes. We hope this article will help professional researchers and radiologists in developing CAD systems for lung cancer detection.

Lung Cancer: Nodules and Masses

Chest Imaging ◽  
2019 ◽  
pp. 263-268
Author(s):  
Ryo E. C. Benson
Keyword(s):  
Lung Cancer ◽  
Lung Nodule ◽  
Primary Lung Cancer ◽  
Pulmonary Nodules ◽  
Lung Mass ◽  
Solitary Pulmonary Nodules ◽  
Solid Part ◽  
Benign Nodules

The chapter titled Nodules and Masses discusses these frequent imaging manifestations of primary lung cancer. A lung nodule is a roughly spherical, circumscribed density that measures < 3 cm. A lung mass is larger than 3 cm. Lung cancer may manifest as a solitary pulmonary nodule or mass. Most solitary pulmonary nodules on radiographs are benign, and the majority represent granulomas and intrapulmonary lymph nodes. Larger lung nodules and lung masses are more likely to be malignant. Nodule assessment includes determination of size, morphology, attenuation, metabolic activity, enhancement characteristics and growth. A solid lung nodule that is stable for 2 years is generally presumed benign. Sub-solid (part-solid and ground-glass nodules) often represent indolent lung cancer, and different follow-up and management guidelines apply. Confident diagnosis of benign nodules such as granulomas is important, as these lesions do not require imaging follow-up.

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