White blood cell (WBC) segmentation, which is important for cytometry, is a challenging issue because of the morphological diversity of WBCs and the complex and uncertain background of blood smear images. This paper proposes a novel method for the nucleus and cytoplasm segmentation of WBCs for cytometry. A color adjustment step was also introduced before segmentation. Color space decomposition and k-means clustering were combined for segmentation. A database including 300 microscopic blood smear images were used to evaluate the performance of our method. The proposed segmentation method achieves 95.7% and 91.3% overall accuracy for nucleus segmentation and cytoplasm segmentation, respectively. Experimental results demonstrate that the proposed method can segment WBCs effectively with high accuracy.

This paper presents a novel method for segmentation of white blood cells (WBCs) in peripheral blood and bone marrow images under different lights through mean shift clustering, color space conversion and nucleus mark watershed operation (NMWO). The proposed method focuses on obtaining seed points. First, color space transformation and image enhancement techniques are used to obtain nucleus groups as inside seeds. Second, mean shift clustering, selection of the C channel component in the CMYK model, and illumination intensity adjustment are employed to acquire WBCs as outside seeds. Third, the seeds and NMWO are employed to precisely determine WBCs and solve the cell adhesion problem. Morphological operations are further used to improve segmentation accuracy. Experimental results demonstrate that the algorithm exhibits higher segmentation accuracy and robustness compared with traditional methods.

Background: Diabetic kidney disease (DKD) is a common and serious complication of diabetes mellitus and has become the most important cause of end-stage renal disease (ESRD). In light of the rising prevalence of diabetes, there is a growing imperative for the early detection and intervention of DKD. With the rapid development of artificial intelligence (AI) technologies, its potential applications in patient education are receiving increasing attention, especially large language models (LLMs). The aim of this study was to evaluate the quality of LLMs-generated patient education materials (PEMs) for early DKD and to explore its feasibility in patient education.Methods: Four LLMs (ERNIE Bot 4.0, GPT-4o, ChatGLM4, and ChatGPT-o1) were selected for this study to generate PEMs. Among them, ERNIE Bot 4.0, GPT-4o, and ChatGLM4 generated 2 versions of PEMs based on American Diabetes Association(ADA) guidelines and without ADA guidelines, respectively. ChatGPT-o1 only generated a PEM without ADA guidelines. An experienced physician wrote a PEM based on ADA guidelines. All materials were assessed using a Likert scale which covered the dimensions of accuracy, completeness, safety, and patient comprehensibility. A total of 7 medical experts (including nephrologists and endocrinologists) and 50 diabetic patients were invited to evaluate the study. We recorded basic information on the patient evaluators.Results: Experts evaluated PEMs from ERNIE Bot 4.0, GPT-4o, ChatGLM4, and ChatGPT-o1, plus physician-sourced PEM. Results showed ERNIE Bot 4.0’s non-guideline PEM and physician-sourced PEM were the top two. Patient assessments of the 2 top-scoring PEMs found that the ERNIE Bot 4.0’s non-guideline PEM performed as well as, if not slightly better than, the physician-sourced PEM in terms of patient comprehensibility, completeness, and safety. In addition, the non-guideline-based PEM was preferred for patients with a history of diabetes longer than 5 years and for patients with proteinuria. Surprisingly, GPT-4o and ChatGLM4’s non-guideline PEMs outperformed guideline-based ones.Conclusion: The LLMs-sourced PEMs, especially the ERNIE Bot 4.0’s non-guideline PEM for early DKD, performed comparably to the physician-sourced PEM in terms of accuracy, completeness, safety, and patient comprehensibility, and exerted a high degree of feasibility. AI may show the potential for broader applications in patient education in the near future.