Academic Journal
Quantitative salivary gland SPECT/CT using deep convolutional neural networks
| العنوان: | Quantitative salivary gland SPECT/CT using deep convolutional neural networks |
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| المؤلفون: | Park, Junyoung, Lee, Jae Sung, Oh, Dongkyu, Ryoo, Hyun Gee, Han, Jeong Hee, Lee, Won Woo |
| المصدر: | Scientific Reports ; volume 11, issue 1 ; ISSN 2045-2322 |
| بيانات النشر: | Springer Science and Business Media LLC |
| سنة النشر: | 2021 |
| الوصف: | Quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) using Tc-99m pertechnetate aids in evaluating salivary gland function. However, gland segmentation and quantitation of gland uptake is challenging. We develop a salivary gland SPECT/CT with automated segmentation using a deep convolutional neural network (CNN). The protocol comprises SPECT/CT at 20 min, sialagogue stimulation, and SPECT at 40 min post-injection of Tc-99m pertechnetate (555 MBq). The 40-min SPECT was reconstructed using the 20-min CT after misregistration correction. Manual salivary gland segmentation for %injected dose (%ID) by human experts proved highly reproducible, but took 15 min per scan. An automatic salivary segmentation method was developed using a modified 3D U-Net for end-to-end learning from the human experts ( n = 333). The automatic segmentation performed comparably with human experts in voxel-wise comparison (mean Dice similarity coefficient of 0.81 for parotid and 0.79 for submandibular, respectively) and gland %ID correlation ( R 2 = 0.93 parotid, R 2 = 0.95 submandibular) with an operating time less than 1 min. The algorithm generated results that were comparable to the reference data. In conclusion, with the aid of a CNN, we developed a quantitative salivary gland SPECT/CT protocol feasible for clinical applications. The method saves analysis time and manual effort while reducing patients’ radiation exposure. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1038/s41598-021-87497-0 |
| الاتاحة: | http://dx.doi.org/10.1038/s41598-021-87497-0 https://www.nature.com/articles/s41598-021-87497-0.pdf https://www.nature.com/articles/s41598-021-87497-0 |
| Rights: | https://creativecommons.org/licenses/by/4.0 ; https://creativecommons.org/licenses/by/4.0 |
| رقم الانضمام: | edsbas.CB48716C |
| قاعدة البيانات: | BASE |
| DOI: | 10.1038/s41598-021-87497-0 |
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