Deep Learning-Based Forearm Subcutaneous Veins Segmentation
In most of the medical treatments, intravenous catheterization is considered as a first crucial phase, in which health practitioners find the superficial vein to conduct blood sampling or medication procedures. In some patients these veins are hard to localize due to different physiological characte...
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| Format: | Article |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9758711/ |
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| author | Zaineb Shah Syed Ayaz Ali Shah Aamir Shahzad Ahmad Fayyaz Shoaib Khaliq Ali Zahir Goh Chuan Meng |
| author_facet | Zaineb Shah Syed Ayaz Ali Shah Aamir Shahzad Ahmad Fayyaz Shoaib Khaliq Ali Zahir Goh Chuan Meng |
| author_sort | Zaineb Shah |
| collection | DOAJ |
| description | In most of the medical treatments, intravenous catheterization is considered as a first crucial phase, in which health practitioners find the superficial vein to conduct blood sampling or medication procedures. In some patients these veins are hard to localize due to different physiological characteristics such as dark skin tone, scars, vein depth etc., which mostly results in multiple attempts for needle insertion. This causes pain, delayed treatment, bleeding, and even infections. To reduce these risks, an automated veins detection method is needed that can efficiently segment the veins and produce realistic results for cannulation purposes. For this purpose, many imaging modalities such as Photoacoustic, Trans-illumination, ultrasound, Near-Infrared etc. are used. Among these modalities Near-Infrared (NIR) imaging modality is considered most suitable due to its lower cost and non-ionizing nature. Over the past few years, subcutaneous veins localization using NIR have attracted increasing attention in the field of health care and biomedical engineering. Therefore, the proposed research work is based on NIR images for forearm subcutaneous veins segmentation. This paper presents a deep learning-based approach called Generative Adversarial Networks (GAN) for segmentation/localization of forearm veins. GANs have shown exciting results in the medical imaging field recently. These are used for unsupervised feature learning and image-to-image translation applications. These networks generate realistic results by learning data mapping from one state to another. Since GANs can produce state of the art results, therefore we have proposed a Pix2Pix GAN for segmentation of forearm veins. The proposed algorithm is trained and tested on forearm subcutaneous veins image dataset. The proposed model outperforms traditional approaches with the mean accuracy and sensitivity, values obtained are 0.971 and 0.862 respectively. The dice coefficient and Intersection over Union (IoU) score are respectively 0.962 and 0.936 which are better than the state-of-the-art methods. |
| first_indexed | 2024-04-13T07:41:04Z |
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| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T07:41:04Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2cee9da92ecd48a08c48b48e0644b6c32022-12-22T02:55:54ZengIEEEIEEE Access2169-35362022-01-0110428144282010.1109/ACCESS.2022.31676919758711Deep Learning-Based Forearm Subcutaneous Veins SegmentationZaineb Shah0https://orcid.org/0000-0003-2809-6545Syed Ayaz Ali Shah1https://orcid.org/0000-0002-3242-8066Aamir Shahzad2Ahmad Fayyaz3https://orcid.org/0000-0001-6109-8214Shoaib Khaliq4https://orcid.org/0000-0003-1377-899XAli Zahir5https://orcid.org/0000-0003-4657-4475Goh Chuan Meng6https://orcid.org/0000-0002-8842-4785Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad, PakistanFaculty of Information and Communication Technology, Universiti Tunku Abdul Rahman (UTAR), Kampar, Perak, MalaysiaIn most of the medical treatments, intravenous catheterization is considered as a first crucial phase, in which health practitioners find the superficial vein to conduct blood sampling or medication procedures. In some patients these veins are hard to localize due to different physiological characteristics such as dark skin tone, scars, vein depth etc., which mostly results in multiple attempts for needle insertion. This causes pain, delayed treatment, bleeding, and even infections. To reduce these risks, an automated veins detection method is needed that can efficiently segment the veins and produce realistic results for cannulation purposes. For this purpose, many imaging modalities such as Photoacoustic, Trans-illumination, ultrasound, Near-Infrared etc. are used. Among these modalities Near-Infrared (NIR) imaging modality is considered most suitable due to its lower cost and non-ionizing nature. Over the past few years, subcutaneous veins localization using NIR have attracted increasing attention in the field of health care and biomedical engineering. Therefore, the proposed research work is based on NIR images for forearm subcutaneous veins segmentation. This paper presents a deep learning-based approach called Generative Adversarial Networks (GAN) for segmentation/localization of forearm veins. GANs have shown exciting results in the medical imaging field recently. These are used for unsupervised feature learning and image-to-image translation applications. These networks generate realistic results by learning data mapping from one state to another. Since GANs can produce state of the art results, therefore we have proposed a Pix2Pix GAN for segmentation of forearm veins. The proposed algorithm is trained and tested on forearm subcutaneous veins image dataset. The proposed model outperforms traditional approaches with the mean accuracy and sensitivity, values obtained are 0.971 and 0.862 respectively. The dice coefficient and Intersection over Union (IoU) score are respectively 0.962 and 0.936 which are better than the state-of-the-art methods.https://ieeexplore.ieee.org/document/9758711/Forearm subcutaneous veinsgenerative adversarial networksimage segmentationmedical image analysis |
| spellingShingle | Zaineb Shah Syed Ayaz Ali Shah Aamir Shahzad Ahmad Fayyaz Shoaib Khaliq Ali Zahir Goh Chuan Meng Deep Learning-Based Forearm Subcutaneous Veins Segmentation IEEE Access Forearm subcutaneous veins generative adversarial networks image segmentation medical image analysis |
| title | Deep Learning-Based Forearm Subcutaneous Veins Segmentation |
| title_full | Deep Learning-Based Forearm Subcutaneous Veins Segmentation |
| title_fullStr | Deep Learning-Based Forearm Subcutaneous Veins Segmentation |
| title_full_unstemmed | Deep Learning-Based Forearm Subcutaneous Veins Segmentation |
| title_short | Deep Learning-Based Forearm Subcutaneous Veins Segmentation |
| title_sort | deep learning based forearm subcutaneous veins segmentation |
| topic | Forearm subcutaneous veins generative adversarial networks image segmentation medical image analysis |
| url | https://ieeexplore.ieee.org/document/9758711/ |
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