A lightweight network for automatic thyroid nodules location and recognition with high speed and accuracy in ultrasound images

Zhou, Sibo; Qiu, Yuxuan; Lin, Han; Liao, Guoliang; Zhuang, Yan; Ma, Buyun; Luo, Yan; Lin, Jie; Chen, Ke

doi:10.3233/xst-221206

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…Therefore, by combining three segmentation evaluation indicators with actual segmentation results and combining quantitative and qualitative analysis, the pooling kernel of the pyramid feature module is set to (1,2,3,6) in this task.…”

Section: The Results Of Multi Organization Segmentationmentioning

confidence: 99%

“…Ultrasound (US) is radiation-free, inexpensive, not risk, real-time imaging and is the most common imaging approach used to examine thyroid disease. While algorithms based on arti cial intelligence (AI) has been widely applied to the diagnosis of thyroid ultrasound images [3][4][5][6][7] . However, the results they obtained were less effective and limited.…”

Section: Introductionmentioning

confidence: 99%

“…demonstrates that the pooling kernels set as(1, 2,3, 6) yields relatively more favourable outcomes, with a Dice score of 0.8087 and an mIOU of 0.7887. Moreover, the pooling kernels set as (1, 2, 4, 8) produces the highest PA index of 0.9446.…”

mentioning

confidence: 96%

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Three-dimensional Visualization of Thyroid Ultrasound Images Based on Multi-scale Features Fusion and Hierarchical Attention

Mi,

Wang,

Feng

et al. 2023

Preprint

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Background The incidence of thyroid disorders is increasing every year. Three-dimensional visualization of the thyroid gland and its surrounding tissues based on free-arm ultrasound scanning videos is essential for identifying the thyroid and related tissues in the neck and screening for associated diseases. However, the key to achieving 3D visualization lies in the multi-target segmentation of neck tissues. Currently, the accuracy of multi-target segmentation for thyroid ultrasound images are unsatisfactory. Method Therefore, in order to enhance the multi-target segmentation accuracy for the thyroid and its surrounding tissues and improve the 3D visualization, in this paper we proposed PA-Unet++. Pyramid pooling module helps in the segmentation of multi-organization with different sizes by integrating multi-scale feature information. Additionally, attention gating mechanism is applied to each decoding layer to progressively highlight the target tissue and suppress the representation of background pixels. Results With 4600 images containing 23,000 annotated regions are divided into training and test sets at a ratio of 9:1, the results show that: compared with the results of U-net++, the Dice of our model increased from 78.78–81.88% (+ 3.10%), the mIOU increased from 73.44–80.35% (+ 6.91%), the PA index increased from 92.95–94.79% (+ 1.84%). Conclusions This indicates that PA-Unet + + has a higher accuracy for segmentation of multiple tissues in thyroid ultrasound images. The segmentation results can optimize the 3D visualization.

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Section: The Results Of Multi Organization Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Three-dimensional Visualization of Thyroid Ultrasound Images Based on Multi-scale Features Fusion and Hierarchical Attention

Mi,

Wang,

Feng

et al. 2023

Preprint

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“…Currently, DL using US images is being used for the diagnosis of breast lesions [28][29][30][31], submandibular gland inflammation [32], Sjögren's syndrome [33], and thyroid nodules [34,35]. The application of DL to the US diagnosis of LNs has been reported for axillary LNs (accuracy = 72.6%) [36] and metastatic LNs from thyroid cancer (accuracy = 83%) [37].…”

Section: Introductionmentioning

confidence: 99%

Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma

Eida,

Fukuda,

Katayama

et al. 2024

Cancers

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Ultrasonography is the preferred modality for detailed evaluation of enlarged lymph nodes (LNs) identified on computed tomography and/or magnetic resonance imaging, owing to its high spatial resolution. However, the diagnostic performance of ultrasonography depends on the examiner’s expertise. To support the ultrasonographic diagnosis, we developed YOLOv7-based deep learning models for metastatic LN detection on ultrasonography and compared their detection performance with that of highly experienced radiologists and less experienced residents. We enrolled 462 B- and D-mode ultrasound images of 261 metastatic and 279 non-metastatic histopathologically confirmed LNs from 126 patients with head and neck squamous cell carcinoma. The YOLOv7-based B- and D-mode models were optimized using B- and D-mode training and validation images and their detection performance for metastatic LNs was evaluated using B- and D-mode testing images, respectively. The D-mode model’s performance was comparable to that of radiologists and superior to that of residents’ reading of D-mode images, whereas the B-mode model’s performance was higher than that of residents but lower than that of radiologists on B-mode images. Thus, YOLOv7-based B- and D-mode models can assist less experienced residents in ultrasonographic diagnoses. The D-mode model could raise the diagnostic performance of residents to the same level as experienced radiologists.

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“…Deep learning (DL) has developed rapidly in recent years and has been widely applied to medical imaging interpretation and intelligent diagnosis, which shows potential for the recognition of thyroid nodules [ 10 ], skin cancer [ 11 ], COVID-19 [ 12 ], and so on. Owing to its high efficiency and accuracy, DL contributes to efficient early screening and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Diagnosis of Multiple Peripheral Retinal Lesions in Ultra-widefield Fundus Images Based on Deep Learning

et al. 2023

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Introduction Compared with traditional fundus examination techniques, ultra-widefield fundus (UWF) images provide 200° panoramic images of the retina, which allows better detection of peripheral retinal lesions. The advent of UWF provides effective solutions only for detection but still lacks efficient diagnostic capabilities. This study proposed a retinal lesion detection model to automatically locate and identify six relatively typical and high-incidence peripheral retinal lesions from UWF images which will enable early screening and rapid diagnosis. Methods A total of 24,602 augmented ultra-widefield fundus images with labels corresponding to 6 peripheral retinal lesions and normal manifestation labelled by 5 ophthalmologists were included in this study. An object detection model named You Only Look Once X (YOLOX) was modified and trained to locate and classify the six peripheral retinal lesions including rhegmatogenous retinal detachment (RRD), retinal breaks (RB), white without pressure (WWOP), cystic retinal tuft (CRT), lattice degeneration (LD), and paving-stone degeneration (PSD). We applied coordinate attention block and generalized intersection over union (GIOU) loss to YOLOX and evaluated it for accuracy, sensitivity, specificity, precision, F 1 score, and average precision (AP). This model was able to show the exact location and saliency map of the retinal lesions detected by the model thus contributing to efficient screening and diagnosis. Results The model reached an average accuracy of 96.64%, sensitivity of 87.97%, specificity of 98.04%, precision of 87.01%, F 1 score of 87.39%, and mAP of 86.03% on test dataset 1 including 248 UWF images and reached an average accuracy of 95.04%, sensitivity of 83.90%, specificity of 96.70%, precision of 78.73%, F 1 score of 81.96%, and mAP of 80.59% on external test dataset 2 including 586 UWF images, showing this system performs well in distinguishing the six peripheral retinal lesions. Conclusion Focusing on peripheral retinal lesions, this work proposed a deep learning model, which automatically recognized multiple peripheral retinal lesions from UWF images and localized exact positions of lesions. Therefore, it has certain potential for early screening and intelligent diagnosis of peripheral retinal lesions. Supplementary Information The online version contains supplementary material available at 10.1007/s40123-023-00651-x.

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A lightweight network for automatic thyroid nodules location and recognition with high speed and accuracy in ultrasound images

Cited by 6 publications

References 15 publications

Three-dimensional Visualization of Thyroid Ultrasound Images Based on Multi-scale Features Fusion and Hierarchical Attention

Three-dimensional Visualization of Thyroid Ultrasound Images Based on Multi-scale Features Fusion and Hierarchical Attention

Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma

Intelligent Diagnosis of Multiple Peripheral Retinal Lesions in Ultra-widefield Fundus Images Based on Deep Learning

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