Literature DB >> 28567104

The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images.

Hui Xiong1, Laith R Sultan1, Theodore W Cary1, Susan M Schultz1, Ghizlane Bouzghar1, Chandra M Sehgal1.   

Abstract

PURPOSE: To assess the diagnostic performance of a leak-plugging segmentation method that we have developed for delineating breast masses on ultrasound images.
MATERIALS AND METHODS: Fifty-two biopsy-proven breast lesion images were analyzed by three observers using the leak-plugging and manual segmentation methods. From each segmentation method, grayscale and morphological features were extracted and classified as malignant or benign by logistic regression analysis. The performance of leak-plugging and manual segmentations was compared by: size of the lesion, overlap area (Oa ) between the margins, and area under the ROC curves (Az ).
RESULTS: The lesion size from leak-plugging segmentation correlated closely with that from manual tracing (R2 of 0.91). Oa was higher for leak plugging, 0.92 ± 0.01 and 0.86 ± 0.06 for benign and malignant masses, respectively, compared to 0.80 ± 0.04 and 0.73 ± 0.02 for manual tracings. Overall Oa between leak-plugging and manual segmentations was 0.79 ± 0.14 for benign and 0.73 ± 0.14 for malignant lesions. Az for leak plugging was consistently higher (0.910 ± 0.003) compared to 0.888 ± 0.012 for manual tracings. The coefficient of variation of Az between three observers was 0.29% for leak plugging compared to 1.3% for manual tracings.
CONCLUSION: The diagnostic performance, size measurements, and observer variability for automated leak-plugging segmentations were either comparable to or better than those of manual tracings.

Entities:  

Keywords:  Breast cancer; computer-aided analysis; lesion segmentation; ultrasonography

Year:  2017        PMID: 28567104      PMCID: PMC5438055          DOI: 10.1177/1742271X17690425

Source DB:  PubMed          Journal:  Ultrasound        ISSN: 1742-271X


  17 in total

1.  Effect of a novel segmentation algorithm on radiologists' diagnosis of breast masses using ultrasound imaging.

Authors:  Jia-Wei Tian; Chun-Ping Ning; Yan-Hui Guo; Heng-Da Cheng; Xiang-Long Tang
Journal:  Ultrasound Med Biol       Date:  2011-11-21       Impact factor: 2.998

Review 2.  US of breast masses categorized as BI-RADS 3, 4, and 5: pictorial review of factors influencing clinical management.

Authors:  Sughra Raza; Allison L Goldkamp; Sona A Chikarmane; Robyn L Birdwell
Journal:  Radiographics       Date:  2010-09       Impact factor: 5.333

3.  BI-RADS for sonography: positive and negative predictive values of sonographic features.

Authors:  Andrea S Hong; Eric L Rosen; Mary S Soo; Jay A Baker
Journal:  AJR Am J Roentgenol       Date:  2005-04       Impact factor: 3.959

4.  Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses.

Authors:  Jae H Song; Santosh S Venkatesh; Emily A Conant; Peter H Arger; Chandra M Sehgal
Journal:  Acad Radiol       Date:  2005-04       Impact factor: 3.173

5.  Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy.

Authors:  Berkman Sahiner; Heang-Ping Chan; Marilyn A Roubidoux; Lubomir M Hadjiiski; Mark A Helvie; Chintana Paramagul; Janet Bailey; Alexis V Nees; Caroline Blane
Journal:  Radiology       Date:  2007-01-23       Impact factor: 11.105

6.  Evaluating lesion segmentation on breast sonography as related to lesion type.

Authors:  Gerard Pons; Joan Martí; Robert Martí; Sergi Ganau; Joan Carles Vilanova; J Alison Noble
Journal:  J Ultrasound Med       Date:  2013-09       Impact factor: 2.153

7.  A robust graph-based segmentation method for breast tumors in ultrasound images.

Authors:  Qing-Hua Huang; Su-Ying Lee; Long-Zhong Liu; Min-Hua Lu; Lian-Wen Jin; An-Hua Li
Journal:  Ultrasonics       Date:  2011-08-25       Impact factor: 2.890

8.  Improved differential diagnosis of breast masses on ultrasonographic images with a computer-aided diagnosis scheme for determining histological classifications.

Authors:  Yumi Kashikura; Ryohei Nakayama; Akiyoshi Hizukuri; Aya Noro; Yuki Nohara; Takashi Nakamura; Minori Ito; Hiroko Kimura; Masako Yamashita; Noriko Hanamura; Tomoko Ogawa
Journal:  Acad Radiol       Date:  2013-04       Impact factor: 3.173

9.  ACCOMP: Augmented cell competition algorithm for breast lesion demarcation in sonography.

Authors:  Jie-Zhi Cheng; Yi-Hong Chou; Chiun-Sheng Huang; Yeun-Chung Chang; Chui-Mei Tiu; Fang-Cheng Yeh; Kuei-Wu Chen; Chi-Hsuan Tsou; Chung-Ming Chen
Journal:  Med Phys       Date:  2010-12       Impact factor: 4.071

10.  Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors.

Authors:  Ruey-Feng Chang; Wen-Jie Wu; Woo Kyung Moon; Dar-Ren Chen
Journal:  Breast Cancer Res Treat       Date:  2005-01       Impact factor: 4.872

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