Liver tumour segmentation using contrast-enhanced multi-detector CT data: performance benchmarking of three semiautomated methods.
Objective: Automatic tumour segmentation and volumetry is useful in cancer staging and treatment outcome assessment. This paper presents a performance benchmarking study on liver tumour segmentation for three semiautomatic algorithms: 2D region growing with knowledge-based constraints (A1), 2D voxel...
| Publicado en: | European Radiology Vol. 20; no. 7; pp. 1738 - 1749 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jul2010
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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=105025210&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105025210 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jul2010 vid: 20 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105025210 51242164 NLM20157817 2010681309 10.1007/s00330-010-1712-z NLM20157817 105025210 ppf: 1738 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Liver tumour segmentation using contrast-enhanced multi-detector CT data: performance benchmarking of three semiautomated methods. aug: au: Zhou JY Wong DW Ding F Venkatesh SK Tian Q Qi YY Xiong W Liu JJ Leow WK Zhou, Jia-Yin Wong, Damon W K Ding, Feng Venkatesh, Sudhakar K Tian, Qi Qi, Ying-Yi Xiong, Wei Liu, Jimmy J Leow, Wee-Kheng affil: Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore sug: subj: Algorithms Contrast Media Liver Neoplasms Pathology Liver Neoplasms Radiography Tomography, X-Ray Computed Methods Automation Benchmarking Human Image Processing, Computer Assisted Neoplasm Staging Weights and Measures Body Weights and Measures ab: Objective: Automatic tumour segmentation and volumetry is useful in cancer staging and treatment outcome assessment. This paper presents a performance benchmarking study on liver tumour segmentation for three semiautomatic algorithms: 2D region growing with knowledge-based constraints (A1), 2D voxel classification with propagational learning (A2) and Bayesian rule-based 3D region growing (A3).Methods: CT data from 30 patients were studied, and 47 liver tumours were isolated and manually segmented by experts to obtain the reference standard. Four datasets with ten tumours were used for algorithm training and the remaining 37 tumours for testing. Three evaluation metrics, relative absolute volume difference (RAVD), volumetric overlap error (VOE) and average symmetric surface distance (ASSD), were computed based on computerised and reference segmentations.Results: A1, A2 and A3 obtained mean/median RAVD scores of 17.93/10.53%, 17.92/9.61% and 34.74/28.75%, mean/median VOEs of 30.47/26.79%, 25.70/22.64% and 39.95/38.54%, and mean/median ASSDs of 2.05/1.41 mm, 1.57/1.15 mm and 4.12/3.41 mm, respectively. For each metric, we obtained significantly lower values of A1 and A2 than A3 (P < 0.01), suggesting that A1 and A2 outperformed A3.Conclusions: Compared with the reference standard, the overall performance of A1 and A2 is promising. Further development and validation is necessary before reliable tumour segmentation and volumetry can be widely used clinically. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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