Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT.
The set of criteria called Response Evaluation Criteria In Solid Tumors (RECIST) is used to evaluate the remedial effects of lung cancer, whereby the size of a lesion can be measured in one dimension (diameter). Volumetric evaluation is desirable for estimating the size of a lesion accurately, but t...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 4; pp. 505 - 513 |
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| Autores principales: | , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131471431&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131471431 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2018 vid: 31 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131471431 131471431 131471431 10.1007/s10278-018-0051-5 131471431 ppf: 505 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT. aug: au: Kim, Young Jae Lee, Seung Hyun Lim, Kun Young Kim, Kwang Gi affil: Department of Biomedical Engineering, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, 21565, Incheon, Republic of Korea sug: subj: Signal Processing, Computer Assisted Lung Neoplasms Diagnosis Tomography, X-Ray Computed Thorax Automation Radiologists Descriptive Statistics Intraclass Correlation Coefficient Confidence Intervals P-Value Comparative Studies ab: The set of criteria called Response Evaluation Criteria In Solid Tumors (RECIST) is used to evaluate the remedial effects of lung cancer, whereby the size of a lesion can be measured in one dimension (diameter). Volumetric evaluation is desirable for estimating the size of a lesion accurately, but there are several constraints and limitations to calculating the volume in clinical trials. In this study, we developed a method to detect lesions automatically, with minimal intervention by the user, and calculate their volume. Our proposed method, called a spherical region-growing method (SPRG), uses segmentation that starts from a seed point set by the user. SPRG is a modification of an existing region-growing method that is based on a sphere instead of pixels. The SPRG method detects lesions while preventing leakage to neighboring tissues, because the sphere is grown, i.e., neighboring voxels are added, only when all the voxels meet the required conditions. In this study, two radiologists segmented lung tumors using a manual method and the proposed method, and the results of both methods were compared. The proposed method showed a high sensitivity of 81.68-84.81% and a high dice similarity coefficient (DSC) of 0.86-0.88 compared with the manual method. In addition, the SPRG intraclass correlation coefficient (ICC) was 0.998 (CI 0.997-0.999, p < 0.01), showing that the SPRG method is highly reliable. If our proposed method is used for segmentation and volumetric measurement of lesions, then objective and accurate results and shorter data analysis time are possible. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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