A Comparative Study for 2D and 3D Computer-aided Diagnosis Methods for Solitary Pulmonary Nodules

Many computer-aided diagnosis (CAD) methods, including 2D and 3D approaches, have been proposed for solitary pulmonary nodules (SPNs). However, the detection and diagnosis of SPNs remain challenging in many clinical circumstances. One goal of this work is to investigate the relative diagnostic accuracy of 2D and 3D methods. An additional goal is to develop a two-stage approach that combines the simplicity of 2D and the accuracy of 3D methods. The experimental results show statistically significant differences between the diagnostic accuracy of 2D and 3D methods. The results also show that with a very minor drop in diagnostic performance the two-stage approach can significantly reduce the number of nodules needed to be processed by the 3D method, streamlining the computational demand

An automated system for lung nodule detection in low-dose computed tomography

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, a dot-enhancement filter for nodule candidate selection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The results obtained on the collected database of low-dose thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.Comment: 9 pages, 9 figures; Proceedings of the SPIE Medical Imaging Conference, 17-22 February 2007, San Diego, California, USA, Vol. 6514, 65143

Multi-scale analysis of lung computed tomography images

A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.Comment: 18 pages, 12 low-resolution figure

Detection of pulmonary nodules by computer-aided diagnosis in multidetector computed tomography: preliminary study of 24 cases

OBJECTIVES: To evaluate the performance of a computer program designed to facilitate the detection of pulmonary nodules using multidetector computed tomography (MDCT) scans of the chest. METHODS: We evaluated 24 consecutive MDCT scans of the chest at the Fleury Diagnostic Imaging Center during the period from October 7 to October 19 of 2006, using a 64-channel CT scanner. The study comprised 12 females and 12 males, ranging from 35 to 77 years of age (mean, 57.9 years). Double reading and a computer-aided diagnosis (CAD) system were used in order to perform two independent analyses of the data. The nodules found using both methods were recorded, and the data were compared. RESULTS: The total sensitivity of CAD for the detection of nodules was 16.5%, increasing to 55% when nodules 1 cm. More than 99% of true nodules detected by CAD were registered in the image double reading process. CONCLUSIONS: In this preliminary 24-case study, the sensitivity of computer program tested was not significantly greater than that of the double-reading process that is routinely performed in this facility.OBJETIVOS: Avaliar o desempenho de um programa para auxílio na detecção de nódulos pulmonares em tomografia computadorizada com múltiplos detectores (TCMD). MÉTODOS: Foram avaliadas 24 tomografias computadorizadas de tórax consecutivas realizadas no Centro de Medicina Diagnóstica Fleury no período de 07/10/2006 a 19/10/2006 usando um tomógrafo helicoidal multidetectores de 64 canais. O estudo compreendeu 12 pacientes do sexo feminino e 12 do sexo masculino, com idades variando entre 35 e 77 anos, idade média de 57,9. As imagens foram analisadas independentemente pelo método da dupla leitura e pelo programa diagnóstico auxiliado por computador (DAC). Os nódulos encontrados nos diferentes processos foram registrados e os dados comparados. RESULTADOS: A sensibilidade total da detecção de nódulos pelo DAC nesse trabalho foi de 16,5%, 55% excluindo os nódulos medindo 1 cm. Menos de 1% dos nódulos verdadeiros destacados pelo DAC não haviam sido registrados no processo de dupla leitura. CONCLUSÕES: Neste trabalho preliminar de 24 casos, o programa testado não conseguiu superar de forma significativa a sensibilidade da dupla leitura realizada de rotina neste serviço.Universidade Federal de São Paulo (UNIFESP) Departamento de Diagnóstico por ImagemCentro de Medicina Diagnóstica FleuryUniversidade Federal de São Paulo (UNIFESP)UNIFESP, Depto. de Diagnóstico por ImagemUNIFESPSciEL

Potential of Computer-Aided Diagnosis to Improve CT Lung Cancer Screening

The development of low-dose spiral computed tomography (CT) has rekindled hope that effective lung cancer screening might yet be found. Screening is justified when there is evidence that it will extend lives at reasonable cost and acceptable levels of risk. A screening test should detect all extant cancers while avoiding unnecessary workups. Thus optimal screening modalities have both high sensitivity and specificity. Due to the present state of technology, radiologists must opt to increase sensitivity and rely on follow-up diagnostic procedures to rule out the incurred false positives. There is evidence in published reports that computer-aided diagnosis technology may help radiologists alter the benefit-cost calculus of CT sensitivity and specificity in lung cancer screening protocols. This review will provide insight into the current discussion of the effectiveness of lung cancer screening and assesses the potential of state-of-the-art computer-aided design developments

Automated detection of lung nodules in low-dose computed tomography

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector computed-tomography (CT) images has been developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, consisting in a 3D dot-enhancement filter for nodule detection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The database used in this study consists of 17 low-dose CT scans reconstructed with thin slice thickness (~300 slices/scan). The preliminary results are shown in terms of the FROC analysis reporting a good sensitivity (85% range) for both internal and sub-pleural nodules at an acceptable level of false positive findings (1-9 FP/scan); the sensitivity value remains very high (75% range) even at 1-6 FP/scanComment: 4 pages, 2 figures: Proceedings of the Computer Assisted Radiology and Surgery, 21th International Congress and Exhibition, Berlin, Volume 2, Supplement 1, June 2007, pp 357-35

Automatic 3D pulmonary nodule detection in CT images: a survey

This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks