Quantitative ultrasound parameters from scattering and propagation may reduce the biopsy rate for breast tumor

Han, Jiaqi; Sun, Pengfei; Sun, Qizhen; Xie, Zhun; Xu, Lijun; Hu, Xiangdong; Ma, Jianguo

doi:10.1016/j.ultras.2023.107233

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…8 Segmentation with B-mode images, however, presents considerable challenges when performed manually by medical professionals. 9,10 Manual segmentation is time-consuming, subjective, and prone to errors, with inconsistencies often arising from the interpretive variability among different observers. 11 These issues are exacerbated in ultrasound imaging due to factors such as low image clarity, artifacts, and the subtleties of endometrial morphology, which can complicate the identification and delineation processes.…”

Section: Introductionmentioning

confidence: 99%

Segment anything with inception module for automated segmentation of endometrium in ultrasound images

Qiu,

Xie,

Jiang

et al. 2024

J. Med. Imag.

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Accurate segmentation of the endometrium in ultrasound images is essential for gynecological diagnostics and treatment planning. Manual segmentation methods are time-consuming and subjective, prompting the exploration of automated solutions. We introduce "segment anything with inception module" (SAIM), a specialized adaptation of the segment anything model, tailored specifically for the segmentation of endometrium structures in ultrasound images.Approach: SAIM incorporates enhancements to the image encoder structure and integrates point prompts to guide the segmentation process. We utilized ultrasound images from patients undergoing hysteroscopic surgery in the gynecological department to train and evaluate the model.Results: Our study demonstrates SAIM's superior segmentation performance through quantitative and qualitative evaluations, surpassing existing automated methods. SAIM achieves a dice similarity coefficient of 76.31% and an intersection over union score of 63.71%, outperforming traditional task-specific deep learning models and other SAM-based foundation models. Conclusions:The proposed SAIM achieves high segmentation accuracy, providing high diagnostic precision and efficiency. Furthermore, it is potentially an efficient tool for junior medical professionals in education and diagnosis.

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Section: Introductionmentioning

confidence: 99%

Segment anything with inception module for automated segmentation of endometrium in ultrasound images

Qiu,

Xie,

Jiang

et al. 2024

J. Med. Imag.

Self Cite

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Spectral Ultrasound Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Sun,

Han,

et al. 2024

J of Ultrasound Medicine

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ObjectivesTo explore the clinical value of the nomogram based on spectral Doppler ultrasound combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer.MethodsWe prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively.ResultsUnivariate analysis showed that lymph node metastasis was related with tumor size, Ki‐67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki‐67, axillary ultrasound, quantitative parameter (the mean of the mid‐band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki‐67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C‐index of 0.83 (95% confidence interval 0.73–0.93).ConclusionAxillary ultrasound combined with Ki‐67 and spectral ultrasound parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.

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Ultrasound Radiofrequency Image Improves the Tissue Segmentation Performance of Deep Learning Models

Xie,

Ji,

et al. 2024

2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS)

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Quantitative ultrasound parameters from scattering and propagation may reduce the biopsy rate for breast tumor

Cited by 5 publications

References 32 publications

Segment anything with inception module for automated segmentation of endometrium in ultrasound images

Segment anything with inception module for automated segmentation of endometrium in ultrasound images

Spectral Ultrasound Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Ultrasound Radiofrequency Image Improves the Tissue Segmentation Performance of Deep Learning Models

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