Search

U-Net and its variants have showcased exceptional performance in the domain of breast medical image segmentation. By employing a fully convolutional network (FCN) structure for semantic segmentation, the symmetrical structure of U-Net offers remarkable flexibility and adaptability. It can be tailored to diverse image segmentation tasks and challenges by adjusting network depth and incorporating new modules, leaving a significant impact on subsequent network designs. This paper aims to delve into the application of U-shaped convolutional networks in breast medical image segmentation, categorizing and summarizing U-shaped convolutional networks used for this purpose in recent years. It outlines the widely used breast medical image datasets and evaluation metrics, discusses common data augmentation techniques, and provides a detailed introduction to the network structure of the U-Net model along with traditional segmentation methods for breast medical images. Furthermore, it summarizes the improvements made to the U-Net network structure for breast medical image segmentation, including modifications like residual structures, multi-scale features, dilation mechanisms, attention mechanisms, skip connection mechanisms, and integration with Transformers. Finally, it addresses the current challenges and problems encountered in breast medical image segmentation and offers insights into future research directions.

It is crucial for understanding mechanisms of drug action to quantify the three-dimensional (3D) drug distribution within a single cell at nanoscale resolution. Yet it remains a great challenge due to limited lateral resolution, detection sensitivities, and reconstruction problems. The preferable method is using X-ray nano-computed tomography (Nano-CT) to observe and analyze drug distribution within cells, but it is time-consuming, requiring specialized expertise, and often subjective, particularly with ultrasmall metal nanoparticles (NPs). Furthermore, the accuracy of batch data analysis through conventional processing methods remains uncertain. In this study, we used radioenhancer ultrasmall HfO 2 nanoparticles as a model to develop a modular and automated deep learning aided Nano-CT method for the localization quantitative analysis of ultrasmall metal NPs uptake in cancer cells. We have established an ultrasmall objects segmentation method for 3D Nano-CT images in single cells, which can highly sensitively analyze minute NPs and even ultrasmall NPs in single cells. We also constructed a localization quantitative analysis method, which may accurately segment the intracellularly bioavailable particles from those of the extracellular space and intracellular components and NPs. The high bioavailability of HfO 2 NPs in tumor cells from deeper penetration in tumor tissue and higher tumor intracellular uptake provide mechanistic insight into HfO 2 NPs as advanced radioenhancers in the combination of quantitative subcellular image analysis with the therapeutic effects of NPs on 3D tumor spheroids and breast cancer. Our findings unveil the substantial uptake rate and subcellular quantification of HfO 2 NPs by the human breast cancer cell line (MCF-7). This revelation explicates the notable efficacy and safety profile of HfO 2 NPs in tumor treatment. These findings demonstrate that this 3D imaging technique promoted by the deep learning algorithm has the potential to provide localization quantitative information about the 3D distributions of specific molecules at the nanoscale level. This study provides an approach for exploring the subcellular quantitative analysis of NPs in single cells, offering a valuable quantitative imaging tool for minute amounts or ultrasmall NPs.

Background: The measurement of posterior tibial slopes (PTS) can aid in the screening and prevention of anterior cruciate ligament (ACL) injuries and improve the success rate of some other knee surgeries. However, the circle method for measuring PTS on magnetic resonance imaging (MRI) scans is challenging and time-consuming for most clinicians to implement in practice, despite being highly repeatable. Currently, there is no automated measurement scheme based on this method. To enhance measurement efficiency, consistency, and reduce errors resulting from manual measurements by physicians, this study proposes two novel, precise, and computationally efficient pipelines for autonomous measurement of PTS., Methods: The first pipeline employs traditional algorithms with experimental parameters to extract the tibial contour, detect adhesions, and then remove these adhesions from the extracted contour. A cyclic process is employed to adjust the parameters adaptively and generate a better binary image for the following tibial contour extraction step. The second pipeline utilizes deep learning models for classifying MRI slice images and segmenting tibial contours. The incorporation of deep learning models greatly simplifies the corresponding steps in pipeline 1., Results: To evaluate the practical performance of the proposed pipelines, doctors utilized MRI images from 20 patients. The success rates of pipeline 1 for central, medial, and lateral slices were 85%, 100%, and 90%, respectively, while pipeline 2 achieved success rates of 100%, 100%, and 95%. Compared to the 10 minutes required for manual measurement, our automated methods enable doctors to measure PTS within 10 seconds., Conclusions: These evaluation results validate that the proposed pipelines are highly reliable and effective. Employing these tools can effectively prevent medical practitioners from being burdened by monotonous and repetitive manual measurement procedures, thereby enhancing both the precision and efficiency. Additionally, this tool holds the potential to contribute to the researches regarding the significance of PTS, particularly those demanding extensive and precise PTS measurement outcomes., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1799/coif). The authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)