M5L: A web-based Computer Aided Detection system for automated search of lung nodules in thoracic Computed Tomography scans

2015 ◽  
pp. 193-199 ◽  
Author(s):  
A Traverso ◽  
M Agnello ◽  
P Cerello ◽  
M Saletta ◽  
S Bagnasco ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Detection System ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Web Based ◽  
Thoracic Computed Tomography ◽  
Computed Tomography Scans ◽  
Computer Aided ◽  
Automated Search

A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies

2018 ◽  
Vol 29 (1) ◽  
pp. 144-152 ◽  
Author(s):  
Lorenzo Vassallo ◽  
Alberto Traverso ◽  
Michelangelo Agnello ◽  
Christian Bracco ◽  
Delia Campanella ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Detection System ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Computed Tomography Scans ◽  
Computer Aided ◽  
Thoracic Malignancies

scholarly journals Computer-aided detection system for lung cancer in computed tomography scans: Review and future prospects

2014 ◽  
Vol 13 (1) ◽  
pp. 41 ◽  
Author(s):  
Macedo Firmino ◽  
Antônio H Morais ◽  
Roberto M Mendoça ◽  
Marcel R Dantas ◽  
Helio R Hekis ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Detection System ◽  
Computer Aided Detection ◽  
Future Prospects ◽  
Computed Tomography Scans ◽  
Computer Aided

Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography

2020 ◽  
Vol 16 (1) ◽  
pp. 16-26 ◽  
Author(s):  
Shabana Rasheed Ziyad ◽  
Venkatachalam Radha ◽  
Thavavel Vayyapuri
Keyword(s):  
Computed Tomography ◽  
Review Paper ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

scholarly journals An Architecture for Computer-Aided Detection and Radiologic Measurement of Lung Nodules in Clinical Trials

2007 ◽  
Vol 4 ◽  
pp. 117693510700400 ◽  
Author(s):  
Matthew S. Brown ◽  
Richard Pais ◽  
Peiyuan Qing ◽  
Sumit Shah ◽  
Michael F. McNitt-Gray ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Detection System ◽  
Volume Measurement ◽  
Volumetric Analysis ◽  
Data Management System ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Nodule Detection ◽  
Computer Aided ◽  
Volume Measurements

Computer tomography (CT) imaging plays an important role in cancer detection and quantitative assessment in clinical trials. High-resolution imaging studies on large cohorts of patients generate vast data sets, which are infeasible to analyze through manual interpretation. In this article we describe a comprehensive architecture for computer-aided detection (CAD) and surveillance on lung nodules in CT images. Central to this architecture are the analytic components: an automated nodule detection system, nodule tracking capabilities and volume measurement, which are integrated within a data management system that includes mechanisms for receiving and archiving images, a database for storing quantitative nodule measurements and visualization, and reporting tools. We describe two studies to evaluate CAD technology within this architecture, and the potential application in large clinical trials. The first study involves performance assessment of an automated nodule detection system and its ability to increase radiologist sensitivity when used to provide a second opinion. The second study investigates nodule volume measurements on CT made using a semi-automated technique and shows that volumetric analysis yields significantly different tumor response classifications than a 2D diameter approach. These studies demonstrate the potential of automated CAD tools to assist in quantitative image analysis for clinical trials.

Techniques for the Automated Segmentation of Lung in Thoracic Computed Tomography Scans

2013 ◽  
pp. 675-687
Author(s):  
William F. Sensakovic ◽  
Samuel G. Armato
Keyword(s):  
Computed Tomography ◽  
Image Data ◽  
Segmentation Method ◽  
Correlation Filters ◽  
Lung Segmentation ◽  
Thoracic Diseases ◽  
Thoracic Computed Tomography ◽  
Segmentation Accuracy ◽  
Computed Tomography Scans ◽  
Computer Aided

Computed Tomography (CT) is widely used to diagnose and assess thoracic diseases. The improved resolution of CT studies has resulted in a substantial increase of image data for analysis by radiologists. The time-consuming nature of this analysis motivates the application of Computer-Aided Diagnostic (CAD) methods to assist radiologists. Most CAD methods require identification of the lung within the patient images, a preprocessing step known as “lung segmentation.” This chapter describes an intensity-based lung segmentation method. The segmentation method begins with simple thresholding, and several image processing modules are included to improve segmentation accuracy and robustness. Common segmentation difficulties are discussed and motivate the inclusion of each module in the lung segmentation method. These modules will include brief explanations of common techniques (e.g., morphological operators) in addition to novel techniques developed specifically for lung segmentation (e.g., gradient correlation filters).

scholarly journals Computer aided detection of epidural masses on computed tomography scans

2014 ◽  
Vol 38 (7) ◽  
pp. 606-612 ◽  
Author(s):  
Jiamin Liu ◽  
Sanket Pattanaik ◽  
Jianhua Yao ◽  
Evrim Turkbey ◽  
Weidong Zhang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Computer Aided Detection ◽  
Computed Tomography Scans ◽  
Computer Aided
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