https://scholars.lib.ntu.edu.tw/handle/123456789/554016
Title: | Computer-aided US diagnosis of breast lesions by using cell-based contour grouping | Authors: | Cheng, J.-Z. Chou, Y.-H. CHIUN-SHENG HUANG YEUN-CHUNG CHANG Tiu, C.-M. Chen, K.-W. CHUNG-MING CHEN |
Issue Date: | 2010 | Journal Volume: | 255 | Journal Issue: | 3 | Start page/Pages: | 746-754 | Source: | Radiology | Abstract: | Purpose: To develop a computer-aided diagnostic algorithm with automatic boundary delineation for differential diagnosis of benign and malignant breast lesions at ultrasonography (US) and investigate the effect of boundary quality on the performance of a computer-aided diagnostic algorithm. Materials and Methods: This was an institutional review board-approved retrospective study with waiver of informed consent. A cellbased contour grouping (CBCG) segmentation algorithm was used to delineate the lesion boundaries automatically. Seven morphologic features were extracted. The classifier was a logistic regression function. Five hundred twenty breast US scans were obtained from 520 subjects (age range, 15-89 years), including 275 benign (mean size, 15 mm; range, 5-35 mm) and 245 malignant (mean size, 18 mm; range, 8-29 mm) lesions. The newly developed computer-aided diagnostic algorithm was evaluated on the basis of boundary quality and differentiation performance. The segmentation algorithms and features in two conventional computer-aided diagnostic algorithms were used for comparative study. Results: The CBCG-generated boundaries were shown to be comparable with the manually delineated boundaries. The area under the receiver operating characteristic curve (AUC) and differentiation accuracy were 0.968 ± 0.010 and 93.1% ± 0.7, respectively, for all 520 breast lesions. At the 5% significance level, the newly developed algorithm was shown to be superior to the use of the boundaries and features of the two conventional computer-aided diagnostic algorithms in terms of AUC (0.974 ± 0.007 versus 0.890 ± 0.008 and 0.788 ± 0.024, respectively). Conclusion: The newly developed computer-aided diagnostic algorithm that used a CBCG segmentation method to measure boundaries achieved a high differentiation performance. ? RSNA, 2010. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/554016 | ISSN: | 0033-8419 | DOI: | 10.1148/radiol.09090001 | SDG/Keyword: | adolescent; adult; aged; algorithm; article; breast cancer; breast disease; breast lesion; cell based contour grouping; computer assisted diagnosis; differential diagnosis; echography; female; human; image quality; intermethod comparison; major clinical study; priority journal; tumor volume; Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Breast Diseases; Diagnosis, Computer-Assisted; Diagnosis, Differential; Female; Humans; Logistic Models; Middle Aged; Retrospective Studies; ROC Curve; Ultrasonography, Mammary |
Appears in Collections: | 醫學院附設醫院 (臺大醫院) |
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