Ellipse shape prior based anti-noise network for parathyroid detection

Wanling Liu; Zhongsheng Cai; Fei Chen; Bo Wang; Wenxin Zhao; Wenhuan Lu

doi:10.1117/12.2680714

27 June 2023 Ellipse shape prior based anti-noise network for parathyroid detection

Wanling Liu, Zhongsheng Cai, Fei Chen, Bo Wang, Wenxin Zhao, Wenhuan Lu

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127052Q (2023) https://doi.org/10.1117/12.2680714
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Endoscopic thyroid surgery is a common treatment method for thyroid diseases. However, due to the small size of parathyroid gland, its unstable position during surgery, and the similar color and texture of the parathyroid gland to its surrounding tissues and organs, parathyroid glands are extremely difficult to be visualized and detected during the operation, leading to accidental damages which result in abnormal parathyroid hormone secretion after surgery. Therefore, detecting the parathyroid glands and preventing damage during endoscopic thyroid surgery is critical. The current target detection technology using SOAT algorithm can help detecting the small parathyroid area, but the accuracy and efficiency are often unsatisfied because of the high image noise. In this research, we propose a PGNet model by applying an anti-noise feature extraction module to prevent the sharp drop in the accuracy of the model under the noisy environment. The anti-noise feature extraction module is based on residual structure, and hence the capability of parathyroid shape detection can be significantly improved. To further improve the detection accuracy of the parathyroid gland, an ellipse anchor frame and an improved IoU loss function have been applied in this PGNet model. The detection accuracy of PGNet model has been verified using data obtained in real-time surgical scenes, and the results show that the PGNet model can achieve 89.3% parathyroid detection accuracy when the IoU threshold is 0.5, which is better than the current SOTA CNN model.

Citation Download Citation

Wanling Liu, Zhongsheng Cai, Fei Chen, Bo Wang, Wenxin Zhao, and Wenhuan Lu "Ellipse shape prior based anti-noise network for parathyroid detection", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127052Q (27 June 2023); https://doi.org/10.1117/12.2680714

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KEYWORDS

Object detection

Target detection

Feature extraction

Data modeling

Surgery

Deep learning

Detection and tracking algorithms

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