用自监督模型自动识别胎儿脑部超声中的脑室扩张，准确率超97%。
Deep Learning Analysis of Prenatal Ultrasound for Identification of Ventriculomegaly
- 基于37万张超声图预训练的视觉变压器模型，微调后用于脑室扩张检测
- 在独立测试集上达到91.78%的F1分数和97.24%准确率，显著优于基线模型
- 热力图显示模型关注脑室区域，结果可解释，适合临床辅助诊断场景
本研究旨在开发一种深度学习模型，用于在产前超声图像中检测脑室扩张。脑室扩张是胎儿大脑脑室扩大的产前病症，早期诊断至关重要，因其可能与染色体异常或遗传综合征相关。我们使用团队先前开发的超声自监督基础模型（USF-MAE），该模型基于超过37万张来自OpenUS-46数据集的超声图像进行预训练，将其微调为二分类任务，以区分胎儿脑部超声图像是否正常或存在脑室扩张。模型通过五折交叉验证和独立测试集评估，性能指标包括准确率、精确率、召回率、特异度、F1分数及受试者工作特征曲线下面积（AUC）。结果显示，该模型在五折交叉验证中F1分数达91.76%，独立测试集为91.78%，相比VGG-19、ResNet-50和ViT-B/16分别提升19.37%、16.15%、2.31%、2.56%、5.03%和11.93%。模型平均测试精确率达94.47%，准确率为97.24%。Eigen-CAM热力图表明模型聚焦于脑室区域，具备可解释性与临床合理性。
原文摘要 · Abstract (English)
The proposed study aimed to develop a deep learning model capable of detecting ventriculomegaly on prenatal ultrasound images. Ventriculomegaly is a prenatal condition characterized by dilated cerebral ventricles of the fetal brain and is important to diagnose early, as it can be associated with an increased risk for fetal aneuploidies and/or underlying genetic syndromes. An Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE), recently developed by our group, was fine-tuned for a binary classification task to distinguish fetal brain ultrasound images as either normal or showing ventriculomegaly. The USF-MAE incorporates a Vision Transformer encoder pretrained on more than 370,000 ultrasound images from the OpenUS-46 corpus. For this study, the pretrained encoder was adapted and fine-tuned on a curated dataset of fetal brain ultrasound images to optimize its performance for ventriculomegaly detection. Model evaluation was conducted using 5-fold cross-validation and an independent test cohort, and performance was quantified using accuracy, precision, recall, specificity, F1-score, and area under the receiver operating characteristic curve (AUC). The proposed USF-MAE model reached an F1-score of 91.76% on the 5-fold cross-validation and 91.78% on the independent test set, with much higher scores than those obtained by the baseline models by 19.37% and 16.15% compared to VGG-19, 2.31% and 2.56% compared to ResNet-50, and 5.03% and 11.93% compared to ViT-B/16, respectively. The model also showed a high mean test precision of 94.47% and an accuracy of 97.24%. The Eigen-CAM (Eigen Class Activation Map) heatmaps showed that the model was focusing on the ventricle area for the diagnosis of ventriculomegaly, which has explainability and clinical plausibility.
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