Needle tracking and segmentation in breast ultrasound imaging based on spatio-temporal memory network
IntroductionUltrasound-guided needle biopsy is a commonly employed technique in modern medicine for obtaining tissue samples, such as those from breast tumors, for pathological analysis. However, it is limited by the low signal-to-noise ratio and the complex background of breast ultrasound imaging....
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1519536/full |
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| author | Qiyun Zhang Jiawei Chen Jinhong Wang Haolin Wang Yi He Yi He Bin Li Zhemin Zhuang Huancheng Zeng |
| author_facet | Qiyun Zhang Jiawei Chen Jinhong Wang Haolin Wang Yi He Yi He Bin Li Zhemin Zhuang Huancheng Zeng |
| author_sort | Qiyun Zhang |
| collection | DOAJ |
| description | IntroductionUltrasound-guided needle biopsy is a commonly employed technique in modern medicine for obtaining tissue samples, such as those from breast tumors, for pathological analysis. However, it is limited by the low signal-to-noise ratio and the complex background of breast ultrasound imaging. In order to assist physicians in accurately performing needle biopsies on pathological tissues, minimize complications, and avoid damage to surrounding tissues, computer-aided needle segmentation and tracking has garnered increasing attention, with notable progress made in recent years. Nevertheless, challenges remain, including poor ultrasound image quality, high computational resource requirements, and various needle shape.MethodsThis study introduces a novel Spatio-Temporal Memory Network designed for ultrasound-guided breast tumor biopsy. The proposed network integrates a hybrid encoder that employs CNN-Transformer architectures, along with an optical flow estimation method. From the Ultrasound Imaging Department at the First Affiliated Hospital of Shantou University, we developed a real-time segmentation dataset specifically designed for ultrasound-guided needle puncture procedures in breast tumors, which includes ultrasound biopsy video data collected from 11 patients.ResultsExperimental results demonstrate that this model significantly outperforms existing methods in improving the positioning accuracy of needle and enhancing the tracking stability. Specifically, the performance metrics of the proposed model is as follows: IoU is 0.731, Dice is 0.817, Precision is 0.863, Recall is 0.803, and F1 score is 0.832. By advancing the precision of needle localization, this model contributes to enhanced reliability in ultrasound-guided breast tumor biopsy, ultimately supporting safer and more effective clinical outcomes.DiscussionThe model proposed in this paper demonstrates robust performance in the computer-aided tracking and segmentation of biopsy needles in ultrasound imaging, specifically for ultrasound-guided breast tumor biopsy, offering dependable technical support for clinical procedures. |
| format | Article |
| id | doaj-art-cb4d9bd03b0048dfaec58a7c0afcbaec |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-cb4d9bd03b0048dfaec58a7c0afcbaec2025-01-17T06:51:06ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011410.3389/fonc.2024.15195361519536Needle tracking and segmentation in breast ultrasound imaging based on spatio-temporal memory networkQiyun Zhang0Jiawei Chen1Jinhong Wang2Haolin Wang3Yi He4Yi He5Bin Li6Zhemin Zhuang7Huancheng Zeng8College of Engineering, Shantou University, Shantou, Guangdong, ChinaCollege of Engineering, Shantou University, Shantou, Guangdong, ChinaDepartment of Ultrasound, Shantou Chaonan Minsheng Hospital, Shantou, Guangdong, ChinaCollege of Engineering, Shantou University, Shantou, Guangdong, ChinaShantou University Medical College, Shantou, Guangdong, ChinaDepartment of Ultrasound, Shantou Central Hospital, Shantou, Guangdong, ChinaProduct Development Department, Shantou Institute of Ultrasonic Instruments, Shantou, Guangdong, ChinaCollege of Engineering, Shantou University, Shantou, Guangdong, ChinaThe Breast Center, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong, ChinaIntroductionUltrasound-guided needle biopsy is a commonly employed technique in modern medicine for obtaining tissue samples, such as those from breast tumors, for pathological analysis. However, it is limited by the low signal-to-noise ratio and the complex background of breast ultrasound imaging. In order to assist physicians in accurately performing needle biopsies on pathological tissues, minimize complications, and avoid damage to surrounding tissues, computer-aided needle segmentation and tracking has garnered increasing attention, with notable progress made in recent years. Nevertheless, challenges remain, including poor ultrasound image quality, high computational resource requirements, and various needle shape.MethodsThis study introduces a novel Spatio-Temporal Memory Network designed for ultrasound-guided breast tumor biopsy. The proposed network integrates a hybrid encoder that employs CNN-Transformer architectures, along with an optical flow estimation method. From the Ultrasound Imaging Department at the First Affiliated Hospital of Shantou University, we developed a real-time segmentation dataset specifically designed for ultrasound-guided needle puncture procedures in breast tumors, which includes ultrasound biopsy video data collected from 11 patients.ResultsExperimental results demonstrate that this model significantly outperforms existing methods in improving the positioning accuracy of needle and enhancing the tracking stability. Specifically, the performance metrics of the proposed model is as follows: IoU is 0.731, Dice is 0.817, Precision is 0.863, Recall is 0.803, and F1 score is 0.832. By advancing the precision of needle localization, this model contributes to enhanced reliability in ultrasound-guided breast tumor biopsy, ultimately supporting safer and more effective clinical outcomes.DiscussionThe model proposed in this paper demonstrates robust performance in the computer-aided tracking and segmentation of biopsy needles in ultrasound imaging, specifically for ultrasound-guided breast tumor biopsy, offering dependable technical support for clinical procedures.https://www.frontiersin.org/articles/10.3389/fonc.2024.1519536/fullcomputer-aided diagnosisbreast cancerultrasoundpunch biopsyneedle segmentation |
| spellingShingle | Qiyun Zhang Jiawei Chen Jinhong Wang Haolin Wang Yi He Yi He Bin Li Zhemin Zhuang Huancheng Zeng Needle tracking and segmentation in breast ultrasound imaging based on spatio-temporal memory network Frontiers in Oncology computer-aided diagnosis breast cancer ultrasound punch biopsy needle segmentation |
| title | Needle tracking and segmentation in breast ultrasound imaging based on spatio-temporal memory network |
| title_full | Needle tracking and segmentation in breast ultrasound imaging based on spatio-temporal memory network |
| title_fullStr | Needle tracking and segmentation in breast ultrasound imaging based on spatio-temporal memory network |
| title_full_unstemmed | Needle tracking and segmentation in breast ultrasound imaging based on spatio-temporal memory network |
| title_short | Needle tracking and segmentation in breast ultrasound imaging based on spatio-temporal memory network |
| title_sort | needle tracking and segmentation in breast ultrasound imaging based on spatio temporal memory network |
| topic | computer-aided diagnosis breast cancer ultrasound punch biopsy needle segmentation |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1519536/full |
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