用深度学习自动分割测量腹部器官，准确率超95%。
Deep Learning-Based Automated Workflow for Accurate Segmentation and Measurement of Abdominal Organs in CT Scans
- 融合nnU-Net、U-Net++和3D RCNN实现全自动分割与测量
- 所有器官精度与召回率均超95%，误差值低
- 适合临床影像分析，可减少人工干预
自动化分析CT扫描中的腹部器官测量对提升诊断效率、降低观察者差异至关重要。手动分割肾脏、肝脏、脾脏和前列腺等器官耗时且易不一致，亟需自动化方法。本研究开发并验证了一种基于深度学习的自动化工作流程，用于CT图像中腹部器官的分割与测量，旨在提升临床评估中的准确性、可靠性与效率。该流程结合nnU-Net与U-Net++进行器官分割，再通过3D RCNN模型测量器官体积与尺寸。模型在包含多个患者数据集上训练与评估，采用精确率、召回率及均方误差（MSE）等指标。分割性能经验证可适应不同解剖结构与扫描设备差异。结果表明，该工作流程在所有目标器官上均达到超过95%的精确率与召回率，均方误差较低，预测值与真实值高度一致。整体流程表现出鲁棒性，能精准勾画并量化肾脏、肝脏、脾脏与前列腺。结论显示，该方法提供了一种高效可靠的腹部器官自动测量方案，显著减少人工干预，提升测量一致性，具备广泛临床应用潜力。未来将拓展至其他器官及复杂病理情况。
原文摘要 · Abstract (English)
Background: Automated analysis of CT scans for abdominal organ measurement is crucial for improving diagnostic efficiency and reducing inter-observer variability. Manual segmentation and measurement of organs such as the kidneys, liver, spleen, and prostate are time-consuming and subject to inconsistency, underscoring the need for automated approaches. Purpose: The purpose of this study is to develop and validate an automated workflow for the segmentation and measurement of abdominal organs in CT scans using advanced deep learning models, in order to improve accuracy, reliability, and efficiency in clinical evaluations. Methods: The proposed workflow combines nnU-Net, U-Net++ for organ segmentation, followed by a 3D RCNN model for measuring organ volumes and dimensions. The models were trained and evaluated on CT datasets with metrics such as precision, recall, and Mean Squared Error (MSE) to assess performance. Segmentation quality was verified for its adaptability to variations in patient anatomy and scanner settings. Results: The developed workflow achieved high precision and recall values, exceeding 95 for all targeted organs. The Mean Squared Error (MSE) values were low, indicating a high level of consistency between predicted and ground truth measurements. The segmentation and measurement pipeline demonstrated robust performance, providing accurate delineation and quantification of the kidneys, liver, spleen, and prostate. Conclusion: The proposed approach offers an automated, efficient, and reliable solution for abdominal organ measurement in CT scans. By significantly reducing manual intervention, this workflow enhances measurement accuracy and consistency, with potential for widespread clinical implementation. Future work will focus on expanding the approach to other organs and addressing complex pathological cases.
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