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Automatic Segmentation of Ultrasound Tomography Image. BIOMED RESEARCH INTERNATIONAL 2017; 2017:2059036. [PMID: 29082240 PMCID: PMC5610831 DOI: 10.1155/2017/2059036] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 06/27/2017] [Accepted: 08/07/2017] [Indexed: 01/01/2023]
Abstract
Ultrasound tomography (UST) image segmentation is fundamental in breast density estimation, medicine response analysis, and anatomical change quantification. Existing methods are time consuming and require massive manual interaction. To address these issues, an automatic algorithm based on GrabCut (AUGC) is proposed in this paper. The presented method designs automated GrabCut initialization for incomplete labeling and is sped up with multicore parallel programming. To verify performance, AUGC is applied to segment thirty-two in vivo UST volumetric images. The performance of AUGC is validated with breast overlapping metrics (Dice coefficient (D), Jaccard (J), and False positive (FP)) and time cost (TC). Furthermore, AUGC is compared to other methods, including Confidence Connected Region Growing (CCRG), watershed, and Active Contour based Curve Delineation (ACCD). Experimental results indicate that AUGC achieves the highest accuracy (D = 0.9275 and J = 0.8660 and FP = 0.0077) and takes on average about 4 seconds to process a volumetric image. It was said that AUGC benefits large-scale studies by using UST images for breast cancer screening and pathological quantification.
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Yu S, Wu S, Zhuang L, Wei X, Sak M, Neb D, Hu J, Xie Y. Efficient Segmentation of a Breast in B-Mode Ultrasound Tomography Using Three-Dimensional GrabCut (GC3D). SENSORS (BASEL, SWITZERLAND) 2017; 17:E1827. [PMID: 28786946 PMCID: PMC5580039 DOI: 10.3390/s17081827] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 08/01/2017] [Accepted: 08/04/2017] [Indexed: 01/14/2023]
Abstract
As an emerging modality for whole breast imaging, ultrasound tomography (UST), has been adopted for diagnostic purposes. Efficient segmentation of an entire breast in UST images plays an important role in quantitative tissue analysis and cancer diagnosis, while major existing methods suffer from considerable time consumption and intensive user interaction. This paper explores three-dimensional GrabCut (GC3D) for breast isolation in thirty reflection (B-mode) UST volumetric images. The algorithm can be conveniently initialized by localizing points to form a polygon, which covers the potential breast region. Moreover, two other variations of GrabCut and an active contour method were compared. Algorithm performance was evaluated from volume overlap ratios ( T O , target overlap; M O , mean overlap; F P , false positive; F N , false negative) and time consumption. Experimental results indicate that GC3D considerably reduced the work load and achieved good performance ( T O = 0.84; M O = 0.91; F P = 0.006; F N = 0.16) within an average of 1.2 min per volume. Furthermore, GC3D is not only user friendly, but also robust to various inputs, suggesting its great potential to facilitate clinical applications during whole-breast UST imaging. In the near future, the implemented GC3D can be easily automated to tackle B-mode UST volumetric images acquired from the updated imaging system.
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Affiliation(s)
- Shaode Yu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China.
| | - Shibin Wu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China.
| | - Ling Zhuang
- Department of Oncology, the Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA.
| | - Xinhua Wei
- Department of Radiology, Guangzhou first Hospital, Guangzhou Medical University, Guangzhou 510180, China.
| | - Mark Sak
- Department of Oncology, the Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA.
- Delphinus Medical Technologies, Inc., Plymouth, Detroit, MI 46701, USA.
| | - Duric Neb
- Department of Oncology, the Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA.
- Delphinus Medical Technologies, Inc., Plymouth, Detroit, MI 46701, USA.
| | - Jiani Hu
- Department of Radiology, Wayne State University, Detroit, MI 48201, USA.
| | - Yaoqin Xie
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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