用视觉变压器模型自动分割胰腺肿瘤，准确率超97%。
Performance of a Deep Learning-Based Segmentation Model for Pancreatic Tumors on Public Endoscopic Ultrasound Datasets
- 基于视觉变压器的分割模型，处理1.7万张超声图像。
- 外部验证集上准确率达97.7%，敏感性71.8%。
- 适合医学影像研究者参考，但需注意误判问题。
胰腺癌是侵袭性极强的癌症之一，生存率低。内镜超声（EUS）是关键诊断手段，但受操作者主观性影响。本研究评估了一种基于视觉变压器的深度学习分割模型在胰腺肿瘤中的表现。模型使用USFM框架，以视觉变压器为骨干，在两个公开数据集共17,367张EUS图像上进行五折交叉验证。图像经灰度化、裁剪、缩放至512×512像素后输入。评估指标包括骰子系数（DSC）、交并比（IoU）、敏感性、特异性与准确率。五折交叉验证中，平均DSC为0.651±0.738，IoU为0.579±0.658，敏感性69.8%，特异性98.8%，准确率97.5%。独立外部验证集（350张图像，由放射科医生人工标注）结果显示：DSC为0.657（95%置信区间：0.634–0.769），IoU为0.614（95%置信区间：0.590–0.689），敏感性71.8%，特异性97.7%。结果稳定，但9.7%病例出现多重错误预测。结论：该视觉变压器模型在EUS图像中对胰腺肿瘤分割表现良好。然而，数据集异质性和有限外部验证提示仍需进一步优化、标准化及前瞻性研究。
原文摘要 · Abstract (English)
Background: Pancreatic cancer is one of the most aggressive cancers, with poor survival rates. Endoscopic ultrasound (EUS) is a key diagnostic modality, but its effectiveness is constrained by operator subjectivity. This study evaluates a Vision Transformer-based deep learning segmentation model for pancreatic tumors. Methods: A segmentation model using the USFM framework with a Vision Transformer backbone was trained and validated with 17,367 EUS images (from two public datasets) in 5-fold cross-validation. The model was tested on an independent dataset of 350 EUS images from another public dataset, manually segmented by radiologists. Preprocessing included grayscale conversion, cropping, and resizing to 512x512 pixels. Metrics included Dice similarity coefficient (DSC), intersection over union (IoU), sensitivity, specificity, and accuracy. Results: In 5-fold cross-validation, the model achieved a mean DSC of 0.651 +/- 0.738, IoU of 0.579 +/- 0.658, sensitivity of 69.8%, specificity of 98.8%, and accuracy of 97.5%. For the external validation set, the model achieved a DSC of 0.657 (95% CI: 0.634-0.769), IoU of 0.614 (95% CI: 0.590-0.689), sensitivity of 71.8%, and specificity of 97.7%. Results were consistent, but 9.7% of cases exhibited erroneous multiple predictions. Conclusions: The Vision Transformer-based model demonstrated strong performance for pancreatic tumor segmentation in EUS images. However, dataset heterogeneity and limited external validation highlight the need for further refinement, standardization, and prospective studies.
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