ABSTRACT
Traditional brightness-mode ultrasound imaging is primarily constrained by the low specificity among tissues and the inconsistency among sonographers. The major cause is the imaging method that represents the amplitude of echoes as brightness and ignores other detailed information, leaving sonographers to interpret based on organ contours that depend highly on specific imaging planes. Other ultrasound imaging modalities, color Doppler imaging or shear wave elastography, overlay motion or stiffness information to brightness-mode images. However, tissue-specific scattering properties and spectral patterns remain unknown in ultrasound imaging. Here we demonstrate that the distribution (size and average distance) of scattering particles leads to characteristic wavelet spectral patterns, which enables tissue recognition and high-contrast ultrasound imaging. Ultrasonic wavelet spectra from similar particle distributions tend to cluster in the eigenspace according to principal component analysis, whereas those with different distributions tend to be distinguishable from one another. For each distribution, a few wavelet spectra are unique and act as a fingerprint to recognize the corresponding tissue. Illumination of specific tissues and organs with designated colors according to the recognition results yields high-contrast ultrasound imaging. The fully-colorized tissue-specific ultrasound imaging potentially simplifies the interpretation and promotes consistency among sonographers, or even enables the applicability for non-professionals.
ABSTRACT
Purpose: 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.
ABSTRACT
Breast cancer has become the most common cancer worldwide, and early screening improves the patient's survival rate significantly. Although pathology with needle-based biopsy is the gold standard for breast cancer diagnosis, it is invasive, painful, and expensive. Meanwhile it makes patients suffer from misplacement of the needle, resulting in misdiagnosis and further assessment. Ultrasound imaging is non-invasive and real-time, however, benign and malignant tumors are hard to differentiate in grayscale B-mode images. We hypothesis that breast tumors exhibit characteristic properties, which generates distinctive spectral patterns not only in scattering, but also during propagation. In this paper, we propose a breast tumor classification method that evaluates the spectral pattern of the tissues both inside the tumor and beneath it. First, quantitative ultrasonic parameters of these spectral patterns were calculated as the representation of the corresponding tissues. Second, parameters were classified by the K-Nearest Neighbor machine learning model. This method was verified with an open access dataset as a reference, and applied to our own dataset to evaluate the potential for tumors assessment. With both datasets, the proposed method demonstrates accurate classification of the tumors, which potentially makes it unnecessary for certain patients to take the biopsy, reducing the rate of the painful and expensive procedure.
Subject(s)
Breast Neoplasms , Humans , Female , Ultrasonography , Breast Neoplasms/diagnostic imaging , Breast Neoplasms/pathology , Breast , Biopsy , Biopsy, Needle/methodsABSTRACT
OBJECTIVE: To investigate the expression of B7-H1 on peripheral blood mononuclear cells from patients with chronic HBV infection. METHODS: Immunofluorescent staining and flow cytometry assay were used to measure the expression of B7-H1 on peripheral blood CD3high T cells, CD19high B cells and CD14high monocytes from chronic HBV infected patients. RESULTS: No significant difference was observed in B7-H1 expression on T cells and B cells between chronic HBV infected patients (CHB) and health controls (HC). B7-H1-expressing CD14high cells were significantly increased in chronic HBV-infected patients (19.17-/+11.64)% as compared with healthy controls [(7.30-/+5.49)%, P<0.01]. A significant positive correlation was found between B7-H1 expression on CD14high monocytes and serum ALT levels. CONCLUSION: There is no significant difference in B7-H1 expression on T cells and B cells between CHB patients and healthy subjects. B7-H1, which is up-regulated on monocytes from chronic HBV-infected patients, in positively correlated to serum ALT levels, and may play a role in the persistence of HBV infection.
