基于深度学习的胰腺肿瘤分割模型在公共内镜超声数据集上的性能
Performance of a Deep Learning-Based Segmentation Model for Pancreatic Tumors on Public Endoscopic Ultrasound Datasets
AI总结：
本文提出一种基于视觉Transformer的深度学习模型，用于胰腺肿瘤的内镜超声图像分割，展示了其在两个公共数据集上的性能表现。
摘要：
背景：胰腺癌是侵袭性最强的癌症之一，生存率较差。内镜超声（EUS）是关键的诊断手段，但其效果受操作者主观性限制。本研究评估了一种基于视觉Transformer的深度学习分割模型用于胰腺肿瘤。方法：使用USFM框架的分割模型，以视觉Transformer作为主干，在两个公共数据集的17367张EUS图像上进行训练和验证，采用五折交叉验证。模型在另一个公共数据集的350张EUS图像独立数据集上进行测试，这些图像由放射科医生手动分割。预处理包括灰度转换、裁剪和调整至512x512像素。指标包括Dice相似性系数（DSC）、交并比（IoU）、灵敏度、特异度和准确率。结果：在五折交叉验证中，模型的平均DSC为0.651±0.738，IoU为0.579±0.658，灵敏度为69.8%，特异度为98.8%，准确率为97.5%。对于外部验证集，模型的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），灵敏度为71.8%，特异度为97.7%。结果一致，但9.7%的病例显示出错误的多重预测。结论：基于视觉Transformer的模型在EUS图像中胰腺肿瘤分割中表现出强大的性能。然而，数据集异质性和有限的外部验证突显了进一步的改进、标准化和前瞻性研究的必要性。
英文摘要：
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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