دورية أكاديمية
Ultrasound volume projection image quality selection by ranking from convolutional RankNet.
| العنوان: | Ultrasound volume projection image quality selection by ranking from convolutional RankNet. |
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| المؤلفون: | Lyu, J, Ling, SH, Banerjee, Sunetra, Zheng, JY, Lai, KL, Yang, D, Zheng, YP, Bi, X, Su, S, Chamoli, U |
| بيانات النشر: | Elsevier BV |
| سنة النشر: | 2021 |
| المجموعة: | University of Technology Sydney: OPUS - Open Publications of UTS Scholars |
| مصطلحات موضوعية: | 0903 Biomedical Engineering, 1103 Clinical Sciences, Nuclear Medicine & Medical Imaging |
| الوصف: | Periodic inspection and assessment are important for scoliosis patients. 3D ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. With the generation of a 3D ultrasound volume projection spine image using our Scolioscan system, a series of 2D coronal ultrasound images are produced at different depths with different qualities. Selecting a high quality image from these 2D images is the crucial task for further scoliosis measurement. However, adjacent images are similar and difficult to distinguish. To learn the nuances between these images, we propose selecting the best image automatically, based on their quality rankings. Here, the ranking algorithm we use is a pairwise learning-to-ranking network, RankNet. Then, to extract more efficient features of input images and to improve the discriminative ability of the model, we adopt the convolutional neural network as the backbone due to its high power of image exploration. Finally, by inputting the images in pairs into the proposed convolutional RankNet, we can select the best images from each case based on the output ranking orders. The experimental result shows that convolutional RankNet achieves better than 95.5% top-3 accuracy, and we prove that this performance is beyond the experience of a human expert. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | Print-Electronic; application/pdf |
| اللغة: | English |
| تدمد: | 0895-6111 1879-0771 |
| العلاقة: | Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society; Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2021, 89, pp. 101847; http://hdl.handle.net/10453/145644Test |
| الإتاحة: | http://hdl.handle.net/10453/145644Test |
| حقوق: | info:eu-repo/semantics/embargoedAccess |
| رقم الانضمام: | edsbas.250B38F7 |
| قاعدة البيانات: | BASE |
| تدمد: | 08956111 18790771 |
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