Quantitative salivary gland SPECT/CT using deep convolutional neural networks.

Journal: Scientific reports
Published Date: Apr 9, 2021

Abstract

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 = 0.93 parotid, R = 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.

Authors

  • Junyoung Park
    Department of Biomedical Sciences, College of Medicine, Seoul National University, Seoul 03080, People's Republic of Korea. Department of Nuclear Medicine, College of Medicine, Seoul National University, Seoul 03080, People's Republic of Korea.
  • Jae Sung Lee
    Department of Biomedical Sciences, Seoul National University, Seoul, Korea jaes@snu.ac.kr.
  • Dongkyu Oh
    Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
  • Hyun Gee Ryoo
    Department of Nuclear Medicine, Seoul National University Hospital, 28 Yongon-Dong, Jongno-Gu, Seoul, 110-744, South Korea.
  • Jeong Hee Han
    Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea.
  • Won Woo Lee
    Freshwater Bioresources Utilization Division, Nakdonggang National Institute of Biological Resources, Gyeongbuk 37242, Korea.

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