Ultrasound-guided needle tracking with deep learning: a novel approach with photoacoustic ground truth
Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/173747 |
| _version_ | 1824456065643708416 |
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| author | Hui, Xie Rajendran, Praveenbalaji Ling, Tong Dai, Xianjin Xing, Lei Pramanik, Manojit |
| author2 | School of Chemistry, Chemical Engineering and Biotechnology |
| author_facet | School of Chemistry, Chemical Engineering and Biotechnology Hui, Xie Rajendran, Praveenbalaji Ling, Tong Dai, Xianjin Xing, Lei Pramanik, Manojit |
| author_sort | Hui, Xie |
| collection | NTU |
| description | Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems. |
| first_indexed | 2025-02-19T03:48:11Z |
| format | Journal Article |
| id | ntu-10356/173747 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:48:11Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1737472024-03-01T15:31:49Z Ultrasound-guided needle tracking with deep learning: a novel approach with photoacoustic ground truth Hui, Xie Rajendran, Praveenbalaji Ling, Tong Dai, Xianjin Xing, Lei Pramanik, Manojit School of Chemistry, Chemical Engineering and Biotechnology School of Electrical and Electronic Engineering Engineering Needle tracking Ultrasound imaging Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems. Published version 2024-02-26T06:16:49Z 2024-02-26T06:16:49Z 2023 Journal Article Hui, X., Rajendran, P., Ling, T., Dai, X., Xing, L. & Pramanik, M. (2023). Ultrasound-guided needle tracking with deep learning: a novel approach with photoacoustic ground truth. Photoacoustics, 34, 100575-. https://dx.doi.org/10.1016/j.pacs.2023.100575 2213-5979 https://hdl.handle.net/10356/173747 10.1016/j.pacs.2023.100575 38174105 2-s2.0-85179002811 34 100575 en Photoacoustics © 2023 The Author(s). Published by Elsevier GmbH. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Engineering Needle tracking Ultrasound imaging Hui, Xie Rajendran, Praveenbalaji Ling, Tong Dai, Xianjin Xing, Lei Pramanik, Manojit Ultrasound-guided needle tracking with deep learning: a novel approach with photoacoustic ground truth |
| title | Ultrasound-guided needle tracking with deep learning: a novel approach with photoacoustic ground truth |
| title_full | Ultrasound-guided needle tracking with deep learning: a novel approach with photoacoustic ground truth |
| title_fullStr | Ultrasound-guided needle tracking with deep learning: a novel approach with photoacoustic ground truth |
| title_full_unstemmed | Ultrasound-guided needle tracking with deep learning: a novel approach with photoacoustic ground truth |
| title_short | Ultrasound-guided needle tracking with deep learning: a novel approach with photoacoustic ground truth |
| title_sort | ultrasound guided needle tracking with deep learning a novel approach with photoacoustic ground truth |
| topic | Engineering Needle tracking Ultrasound imaging |
| url | https://hdl.handle.net/10356/173747 |
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