The Lung Image Database Consortium (LIDC) Data Collection Process for Nodule Detection and Annotation

McNitt‐Gray, Michael F.; Armato, Samuel G.; Meyer, Charles R.; Reeves, Anthony P.; McLennan, Geoffrey; Pais, Richie C.; Freymann, John; Brown, Matthew S.; Engelmann, Roger; Bland, Peyton H.; Laderach, Gary E.; Piker, Chris; Guo, Junfeng; Towfic, Zaid J.; Qing, David; Yankelevitz, David F.; Aberle, Denise R.; Beek, Edwin Jacques Rudolph van; MacMahon, Heber; Kazerooni, Ella A.; Croft, Barbara Y.; Clarke, Laurence P.

doi:10.1016/j.acra.2007.07.021

Cited by 196 publications

(104 citation statements)

References 20 publications

(18 reference statements)

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“…The segmentation algorithm is applicable for nodules of various density like solid, part-solid, and non-solid. The performance of the technique is quantitatively evaluated in terms of segmentation overlap on two data set LIDC1 and LIDC2 consisting of 23 and 82 nodules, respectively, from LIDC database [16]. Among 23 nodules in LIDC1, 22 have slice thickness of 0.66 mm.…”

Section: Segmentation Of Solid Part-solid and Non-solid Pulmonary Nmentioning

confidence: 99%

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Mukhopadhyay

2015

J Digit Imaging

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Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The wellcircumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/partsolid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.

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Section: Segmentation Of Solid Part-solid and Non-solid Pulmonary Nmentioning

confidence: 99%

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Mukhopadhyay

2015

J Digit Imaging

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“…Generally, these databases are used to train students, to serve as a repository for rare cases, and to allow comparisons between the performance of different CADe systems [61]. Among the more important public databases available are: Lung Image Database Consortium (LIDC) [62,63], Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) [64,65], Early Lung Cancer Action Program (ELCAP) [7], Nederlands Leuvens Longkanker Screeningsonderzoek (NELSON) [66] and Automatic Nodule Detection 2009 (ANODE09) [67,68].…”

Section: Acquisition Of Datamentioning

confidence: 99%

“…The LIDC radiologists annotations include freehand outlines of nodules ≥ 3 mm in diameter on each CT slice in which the nodules are visible, along with the subjective ratings on a five-or six-point scale of the following pathologic features: calcification, internal structure, subtlety, lobulation, margins, sphericity, malignancy, texture, and spiculation. The annotations also include a single mark (an approximate centroid) of nodules ≤ 3 mm in diameter as well as non-nodules ≥ 3 mm [63,62,69].…”

Section: Acquisition Of Datamentioning

confidence: 99%

“…From the 2,669 lesions marked "nodule ≥ 3 mm" by at least one radiologist, only 928 (34.7%) received similar results from all four radiologists. This shows that the image analysis task is susceptible to many subjective factors, and therefore the false positive reduction step needs to be improved [62]. The elimination of false positives can be performed by analyzing the extracted features of the potential nodule found.…”

Section: False Positive Reductionmentioning

confidence: 99%

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

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

202

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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 * Corresponding author. E-mail: tavares@fe.up.pt. 31, 2015 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. Preprint submitted to Computer Methods and Programs in Biomedicine August

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“…The LIDC developed a pulmonary nodule documentation process [15,16] where expert radiologists marked the visible lesion boundary belonging to each lesion in all of the relevant axial images.…”

Section: Introductionmentioning

confidence: 99%

The Lung Image Database Consortium (LIDC)

Reeves¹,

Biancardi²,

Apanasovich³

et al. 2007

Academic Radiology

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Rationale and Objectives-To investigate the effects of choosing between different metrics in estimating the size of pulmonary nodules as a factor both of nodule characterization and of performance of computer aided detection systems, since the latters are always qualified with respect to a given size range of nodules.Materials and Methods-This study used 265 whole-lung CT scans documented by the Lung Image Database Consortium using their protocol for nodule evaluation. Each inspected lesion was reviewed independently by four experienced radiologists who provided boundary markings for nodules larger than 3 mm. Four size metrics, based on the boundary markings, were considered: a uni-dimensional and two bi-dimensional measures on a single image slice and a volumetric measurement based on all the image slices. The radiologist boundaries were processed and those with four markings were analyzed to characterize the inter-radiologist variation, while those with at least one marking were used to examine the difference between the metrics.Results-The processing of the annotations found 127 nodules marked by all of the four radiologists and an extended set of 518 nodules each having at least one observation with three-dimensional sizes ranging from 2.03 to 29.4 mm (average 7.05 mm, median 5.71 mm). A very high inter-observer variation was observed for all these metrics: 95% of estimated standard deviations were in the following ranges [0. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public AccessAuthor Manuscript Acad Radiol. Author manuscript; available in PMC 2008 December 1. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript the uni-dimensional, and the two bi-dimensional size metrics respectively (in mm). Also a very large difference among the metrics was observed: 0.95 probability-coverage region widths for the volume estimation conditional on uni-dimensional, and the two bi-dimensional size measurements of 10mm were 7.32, 7.72, and 6.29 mm respectively.Conclusions-The selection of data subsets for performance evaluation is highly impacted by the size metric choice. The LIDC plans to include a single size measure for each nodule in its database. This metric is not intended as a gold standard for nodule size; rather, it is intended to facilitate the selection of unique repeatable size limited nodule subsets.

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The Lung Image Database Consortium (LIDC) Data Collection Process for Nodule Detection and Annotation

Abstract: A unique data collection process was developed, tested, and implemented that allowed multiple readers at distributed sites to asynchronously review CT scans multiple times. This process captured the opinions of each reader regarding the location and spatial extent of lung nodules.

Cited by 196 publications

References 20 publications

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

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

The Lung Image Database Consortium (LIDC)

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