A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study.
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 repeata...
| Publicado en: | Journal of Digital Imaging Vol. 29; no. 4; pp. 476 - 488 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2016
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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=116774816&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 116774816 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2016 vid: 29 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 116774816 116774816 116774816 10.1007/s10278-016-9859-z 116774816 ppf: 476 ppct: 12 formats: fmt: @attributes: type: P tig: atl: A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study. aug: au: Kalpathy-Cramer, Jayashree Zhao, Binsheng Goldgof, Dmitry Gu, Yuhua Wang, Xingwei Yang, Hao Tan, Yongqiang Gillies, Robert Napel, Sandy affil: Massachusetts General Hospital and Harvard Medical School, Boston USA sug: subj: Lung Abnormalities Neoplasms Radiography Tomography, Spiral Computed Neoplasms Physiopathology Human ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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