Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is unknown. Validation is key to quantitative applications, especially using limited-bandwidth ultrasonic linear detector arrays. Here, we propose a hybrid Bayesian convolutional neural network (Hybrid-BCNN) to jointly predict PA image and segmentation with error (uncertainty) predictions. Each output pixel represents a probability distribution where error can be quantified. The Hybrid-BCNN was trained with simulated PA data and applied to both simulations and experiments. Due to the sparsity of PA images, segmentation focuses Hybrid-BCNN on minimizing the loss function in regions with PA signals for better predictions. The results show that accurate PA segmentations and images are obtained, and error predictions are highly statistically correlated to actual errors. To leverage error predictions, confidence processing created PA images above a specific confidence level.

利用深度学习提升光声图像重建，通过混合贝叶斯卷积神经网络(Hybrid-BCNN)联合预测光声图像和分割带有误差(不确定性)预测，通过稀疏的光声图像分段实现在带有光声信号的区域中最小化损失函数以获得更好的预测结果，为光声图像选择自信度超过特定阈值的预测结果。

深度学习在光声成像中的联合分割与图像重建及误差预测