MTN-Net: A Multi-Task Network for Detection and Segmentation of Thyroid Nodules in Ultrasound Images

Chen, Leyao; Zheng, Wei; Hu, Wenjing

doi:10.1007/978-3-031-10989-8_18

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Second, we designed the MTN‐Net 10 based on multi‐scale feature fusion for detecting and segmenting thyroid nodules in ultrasound images. The network used the Trident Network 11 as the baseline, where the Trident Block 11 was used to fully capture the multi‐scale features of thyroid nodules so that the network could identify thyroid nodules of different sizes.…”

Section: Methodsmentioning

confidence: 99%

“…The network used the Trident Network 11 as the baseline, where the Trident Block 11 was used to fully capture the multi‐scale features of thyroid nodules so that the network could identify thyroid nodules of different sizes. We also proposed a TN‐NMS 10 algorithm for combining the results of multiple branches, which could effectively suppress the false detection of internal nodules. Finally, a semantic segmentation branch was embedded in the detection network, which could accurately segment thyroid nodules based on the detection results (Figure S2 ).…”

Section: Methodsmentioning

confidence: 99%

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Establishment and preliminary application of personalized three‐dimensional reconstruction of thyroid gland with automatic detection of thyroid nodules based on ultrasound videos

Yandong,

Shiqi,

Lanting

et al. 2024

J Applied Clin Med Phys

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PurposeA well display of the spatial location of thyroid nodules in the thyroid is important for surgical path planning and surgeon‐patient communication. The aim of this study was to establish a three‐dimensional (3D) reconstruction method of the thyroid gland, thyroid nodule, and carotid artery with automatic detection based on two‐dimensional (2D) ultrasound videos, and to evaluate its clinical value.MethodsUltrasound videos, including the thyroid gland with nodule, isthmus of thyroid gland, and ipsilateral carotid artery, were recorded. BC‐UNet, MTN‐Net, and RDPA‐U‐Net network models were innovatively employed for segmentation of the thyroid glands, the thyroid nodules, and the carotid artery respectively. Marching Cubes algorithm was used for reconstruction, while Laplacian smoothing algorithm was employed to smooth the 3D model surface. Using this model, 20 patients and 15 surgeons completed surveys on the effectiveness of this model for the pre‐surgery demonstration of nodule location as well as surgeon‐patient communication.ResultsThe thyroid gland with nodule, isthmus of gland, and carotid artery were reconstructed and displayed. With the 3D model, the understanding of the spatial location of thyroid nodules improved in all three surgeon groups, eliminating the influence of professional levels. In the patient survey, the patients’ understanding of the thyroid nodule location and procedure for surgery were significantly improved. In addition, with the 3D model, the time for doctors to explain to patients was significantly reduced (16.75 vs. 8.85 min, p = 0.001).ConclusionTo our knowledge, this is the first report of constructing a 3D thyroid model using a deep learning technique for personalized thyroid segmentation based on 2D ultrasound videos. The preliminary clinical application showed that it was conducive to the comprehension of the location of thyroid nodules for surgeons and patients, with significant improvement on the efficiency of surgeon‐patient communication.

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Section: Methodsmentioning

confidence: 99%