Volume 1, Issue 1

Breast ultrasound image segmentation is vital in medical imaging, enabling precise delineation of tissues and lesions, which contributes to the diagnosis and treatment of breast diseases. This article reviews both traditional methods and recent advancements in deep learning techniques for breast ultrasound image segmentation. The discussion begins by highlighting the significance of image segmentation in breast disease diagnosis and its background within medical imaging. Traditional segmentation methods, such as thresholding, edge detection, and region growing, are examined, with an analysis of their applications and limitations in breast ultrasound segmentation. Subsequently, the focus shifts to deep learning approaches, including classic models like Convolutional Neural Networks, Fully Convolutional Networks, and U-Net, along with their improved algorithms. These methods learn hierarchical features directly from raw data, reducing reliance on manual preprocessing. U-Net, in particular, is highlighted as the benchmark for medical image segmentation due to its efficient data usage and ability to preserve fine-grained details. Comparative analysis demonstrates the advantages of deep learning in enhancing segmentation accuracy, reducing noise, and handling complex texture structures. The article concludes by summarizing current achievements and challenges in the field, while offering an outlook on the future developments aimed at advancing breast ultrasound image segmentation for improved diagnosis and treatment of breast diseases.

Biological clock genes, which regulate the body’s circadian rhythm, play a crucial role in pancreatic cancer development. The abnormal expression of these genes is closely associated with malignant proliferation, invasion, metastasis, and resistance to chemotherapy. Several biological clock genes in pancreatic cancer tissues contribute to tumor progression by modulating key signaling pathways. Moreover, disruptions in circadian clock genes are significantly linked to poor prognosis and may serve as diagnostic markers and prognostic indicators. This review summarizes recent research on the regulatory mechanisms of biological clock genes in pancreatic cancer, emphasizing their potential clinical applications as diagnostic markers, therapeutic targets, and prognostic tools. These findings may lead to new approaches for personalized pancreatic cancer treatment.