标题： 垂体腺和垂体腺瘤在磁共振成像中自动分割技术的系统综述 显示英文标题

标题： Systematic Review of Pituitary Gland and Pituitary Adenoma Automatic Segmentation Techniques in Magnetic Resonance Imaging

摘要： 目的：从磁共振成像（MRI）中准确分割垂体腺和腺瘤对于垂体腺瘤的诊断和治疗至关重要。 本系统综述评估了自动分割方法，以提高基于MRI的垂体腺瘤及其自身腺体的分割准确性与效率。 方法：我们回顾了34项采用了自动和半自动分割方法的研究。 我们提取并综合了关于分割技术和性能指标（如Dice重叠分数）的数据。 结果：大多数被回顾的研究使用了深度学习方法，其中基于U-Net的模型最为普遍。 自动方法在垂体腺分割中的Dice分数为0.19--89.00%，在腺瘤分割中的Dice分数为4.60--96.41%。 半自动方法在垂体腺分割中的报告分数为80.00--92.10%，在腺瘤分割中的报告分数为75.90--88.36%。 结论：大多数研究未报告重要的指标，如MRI场强、年龄和腺瘤大小。 自动分割技术，如基于U-Net的模型，在腺瘤分割方面显示出前景，但需要进一步改进，以在小结构如正常垂体腺中实现一致的良好性能。 持续的创新和更大、更多样化的数据集可能对提高临床适用性至关重要。 显示英文摘要

摘要： Purpose: Accurate segmentation of both the pituitary gland and adenomas from magnetic resonance imaging (MRI) is essential for diagnosis and treatment of pituitary adenomas. This systematic review evaluates automatic segmentation methods for improving the accuracy and efficiency of MRI-based segmentation of pituitary adenomas and the gland itself. Methods: We reviewed 34 studies that employed automatic and semi-automatic segmentation methods. We extracted and synthesized data on segmentation techniques and performance metrics (such as Dice overlap scores). Results: The majority of reviewed studies utilized deep learning approaches, with U-Net-based models being the most prevalent. Automatic methods yielded Dice scores of 0.19--89.00\% for pituitary gland and 4.60--96.41\% for adenoma segmentation. Semi-automatic methods reported 80.00--92.10\% for pituitary gland and 75.90--88.36\% for adenoma segmentation. Conclusion: Most studies did not report important metrics such as MR field strength, age and adenoma size. Automated segmentation techniques such as U-Net-based models show promise, especially for adenoma segmentation, but further improvements are needed to achieve consistently good performance in small structures like the normal pituitary gland. Continued innovation and larger, diverse datasets are likely critical to enhancing clinical applicability.