Multifunctional aggregation network of cell nuclei segmentation aiming histopathological diagnosis assistance: A new MA-Net construction.

Automated diagnostic systems can enhance the accuracy and efficiency of pathological diagnoses, nuclear segmentation plays a crucial role in computer-aided diagnosis systems for histopathology. However, achieving accurate nuclear segmentation is challenging due to the complex background tissue structures and significant variations in cell morphology and size in pathological images. In this study, we have proposed a U-Net based deep learning model, called MA-Net(Multifunctional Aggregation Network), to accurately segmenting nuclei from H&E stained images. In contrast to previous studies that focused on improving a single module of the network, we applied feature fusion modules, attention gate units, and atrous spatial pyramid pooling to the encoder and decoder, skip connections, and bottleneck of U-Net, respectively, to enhance the network's performance in nuclear segmentation. The dice coefficient loss was used during model training to enhance the network's ability to segment small objects. We applied the proposed MA-Net to multiple public datasets, and comprehensive results showed that this method outperforms the original U-Net method and other state-of-the-art methods in nuclei segmentation tasks. The source code of our work can be found in https://github.com/LinaZhaoAIGroup/MA-Net.

Precise and efficient measurement of tibial slope on magnetic resonance imaging (MRI): two novel autonomous pipelines by traditional and deep learning algorithms.

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.

Quantitative Three-Dimensional Imaging Analysis of HfO2 Nanoparticles in Single Cells via Deep Learning Aided X-ray Nano-Computed Tomography.

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 HfO2 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 HfO2 NPs in tumor cells from deeper penetration in tumor tissue and higher tumor intracellular uptake provide mechanistic insight into HfO2 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 HfO2 NPs by the human breast cancer cell line (MCF-7). This revelation explicates the notable efficacy and safety profile of HfO2 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.

Advantages of transformer and its application for medical image segmentation: a survey.

PURPOSE: Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models. METHODS: This review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results. RESULTS: To better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described. CONCLUSION: Even if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators.

Protonation-mediated DNA tile self-assembly with nuclease resistance characteristic for signal-amplified detection of microRNAs.

DNA nanotechnology, developing rapidly in recent years, has unprecedented superiorities in biological application-oriented research including high programmability, convenient functionalization, reconfigurable structure, and intrinsic biocompatibility. However, the susceptibility to nucleases in the physiological environment has been an obstacle to applying DNA nanostructures in biological science research. In this study, a new DNA self-assembly strategy, mediated by double-protonated small molecules instead of classical metal ions, is developed to enhance the nuclease resistance of DNA nanostructures while retaining their integrality and functionality, and the relative application has been launched in the detection of microRNAs (miRNAs). Faced with low-abundance miRNAs, we integrate hybrid chain reaction (HCR) with DNA self-assembly in the presence of double-protonated small molecules to construct a chemiluminescence detection platform with nuclease resistance, which utilizes the significant difference of molecular weight between DNA arrays and false-positive products to effectively separate of reaction products and remove the detection background. This strategy attaches importance to the nucleic acid stability during the assay process via improving nuclease resistance while rendering the detection results for miRNAs more authentic and reliable, opening our eyes to more possibilities for the multiple applications of customized DNA nanostructures in biology, including bioassay, bioimaging, drug delivery, and cell modulation.

A specific and low background nucleic acids sensing strategy based on rolling circle amplification coupled with a magnetic DNA machine.

We propose a universal fluorescence method for detection of nucleic acids based on rolling circle amplification (RCA) combined with a magnetic DNA machine and using dengue virus nucleic acids as an example target. RCA specifically amplifies the target and yields a large number of initiators employing heat-labile double-stranded DNase. The magnetic DNA machine produces a fluorescence signal and eliminates background noise. This method achieved a wide linear range, promising recovery and ultrahigh recognition specificity for one-base mismatches, and indicates the potential application of this sensing strategy in the clinical diagnosis of nucleic acids of pathogens.

Size-Controlled DNA Tile Self-Assembly Nanostructures Through Caveolae-Mediated Endocytosis for Signal-Amplified Imaging of MicroRNAs in Living Cells.

Signal-amplified imaging of microRNAs (miRNAs) is a promising strategy at the single-cell level because liquid biopsy fails to reflect real-time dynamic miRNA levels. However, the internalization pathways for available conventional vectors predominantly involve endo-lysosomes, showing nonideal cytoplasmic delivery efficiency. In this study, size-controlled 9-tile nanoarrays are designed and constructed by integrating catalytic hairpin assembly (CHA) with DNA tile self-assembly technology to achieve caveolae-mediated endocytosis for the amplified imaging of miRNAs in a complex intracellular environment. Compared with classical CHA, the 9-tile nanoarrays possess high sensitivity and specificity for miRNAs, achieve excellent internalization efficiency by caveolar endocytosis, bypassing lysosomal traps, and exhibit more powerful signal-amplified imaging of intracellular miRNAs. Because of their excellent safety, physiological stability, and highly efficient cytoplasmic delivery, the 9-tile nanoarrays can realize real-time amplified monitoring of miRNAs in various tumor and identical cells of different periods, and imaging effects are consistent with the actual expression levels of miRNAs, ultimately demonstrating their feasibility and capacity. This strategy provides a high-potential delivery pathway for cell imaging and targeted delivery, simultaneously offering a meaningful reference for the application of DNA tile self-assembly technology in relevant fundamental research and medical diagnostics.