Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach

Gu, Yi; Kumar, Virendra; Hall, Lawrence O.; Goldgof, Dmitry B.; Li, Ching-Yen; Korn, R; Bendtsen, Claus; Velazquez, Emmanuel Rios; Dekker, André; Aerts, Hugo J.W.L.; Lambin, Philippe; Li, Xiuli; Tian, Jie; Gatenby, Robert A.; Gillies, Robert J.

doi:10.1016/j.patcog.2012.10.005

Cited by 128 publications

(85 citation statements)

References 39 publications

(32 reference statements)

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“…While all 41 CT volumes have been used in prior studies [21][22][23][25][26][27], they have never been used as a set for the comparison of segmentation algorithms, as we present here, and there therefore is no scientific overlap between the work described here and prior publications.…”

Section: Datasetsmentioning

confidence: 99%

“…A single click ensemble segmentation (SCES) algorithm using a proprietary platform [27] was used for lung nodule segmentation as seen in Fig. 1.…”

Section: Algorithmmentioning

confidence: 99%

“…Raunig [20] Kessler [21] Barnhart [26] Within-subject standard deviation (wSD) σ Kessler [21] Within-subject coefficient of variation (wCV) σ/μ Obuchowski (19 ) Repeatability coefficient (RC) BThe least significant difference between two repeated measurements on a case taken under the same conditions^K essler [21] Obuchowski [19] Bland [27] Intra-class correlation coefficient (ICC) Consistency of repeated measures relative to the total variability in the population (assumes independent normally distributed samples)…”

Section: ] Precisionmentioning

confidence: 99%

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A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study

et al. 2016

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Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 μl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.

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Section: Datasetsmentioning

confidence: 99%

“…A single click ensemble segmentation (SCES) algorithm using a proprietary platform [27] was used for lung nodule segmentation as seen in Fig. 1.…”

Section: Algorithmmentioning

confidence: 99%

Section: ] Precisionmentioning

confidence: 99%

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A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study

et al. 2016

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“…They evaluated the predictive values of more than 400 textural and shape-and intensity-based features extracted from the computed tomography (CT) images acquired before treatment. Tumor volumes were delineated either by radiation oncologists or using semiautomatic segmentation methods [16,17]. A subset of radiomic features were identified for predicting patient survival and describing intra-tumoral heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Radiomic Phenotyping of Cervix Cancer

Anbumani¹

2018

ICPJL

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Radiomics aims to extract huge amount of quantifiable features from medical images with data-characterization algorithms [1][2][3][4][5]. Radiologists cannot appreciate all the disease characteristics in generic diagnostic images [6]. Using these radiomic features, it is possible to uncover the physiognomies of disease progression. The hypothesis of radiomics is that the distinctive imaging features between disease forms may be useful for predicting prognosis and therapeutic response for various conditions, thus providing valuable information for personalized therapy [1,7,8]. Radiomics emerged from oncology [3] is the most cutting-edge innovation. However, the technique can be useful in any medical study involving tomographic imaging. This article analyzes currently available phenotyping algorithms for Lung and Head & Neck malignancies. And It can help researchers to understand the concept to apply the same for cervix cancer.

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“…In prior work, it has been found that the reproducibility of features depend on the robust of segmentation algorithms, which should provide an accurate and reproducible results [5]. Furthermore, semi-automated segmentation have a better similarity index (SI) than manual segmentation (the SI of machine-segmented lesions SI >0.93, whereas the SI of manual segmentation SI was 0.73) [6]. In this article, a semi-automated segmentation method based on CV model was used twice (obtained 2 data set: test and retest) to get tumor region for CT images of 35 NSCLC patients (15 adenocarcinoma and 20 epidermoid carcinoma).…”

Section: Introductionmentioning

confidence: 99%

The Research of Reproducibility and Non-redundancy Feature Selection Methods in Radiomics

Wei¹,

Song²,

Gu³

2017

dtcse

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Abstract. "Radiomics" is a process of extracting a great quantity of descriptive features from biomedical images that can be used for prognosis. One of the major challenges of radiomics is to acquire the features that reproducibility and non-redundancy. The reproducibility of features dependent on the segmentation algorithm, and it should provide an accurate and reproducible result. In this study, a semi-automated segmentation method based on, which has an good effect for gray scale inhomogeneous of tumors was used to get tumor region for computed tomographic (CT) images of 35 non-small cell lung cancer (NSCLC) patients. A set of features (125 3D and 92 2D) was computed for each tumor region in the test/retest data set. In terms of comparing the intra-class correlation coefficient (ICC) with manual segmentation method in feature extracting, it is indicated that the features obtained by the method of on CV model has a better representative. A series quantitative feature of better reproducibility could be obtained. However, these features were redundant. A feature selection method based on sparse representation coefficient (SRC) was used to filter these redundant features. It is indicated that the features obtained by SRC have better non-redundancy through comparison the selection methods based on Pearson correlation coefficient (PCC) and symmetrical uncertainty (SU). Thus quantitative image features that reproducibility and non-redundancy provide informative and prognostic biomarkers for NSCLC.

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Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach

Cited by 128 publications

References 39 publications

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study

Quantitative Radiomic Phenotyping of Cervix Cancer

The Research of Reproducibility and Non-redundancy Feature Selection Methods in Radiomics

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