Joint Segmentation and Image Reconstruction with Error Prediction in Photoacoustic Imaging using Deep Learning
This work addresses validation issues in quantitative photoacoustic imaging, particularly for limited-bandwidth detector arrays, but is incremental as it builds on existing deep learning methods.
The paper tackles the challenge of quantifying errors in photoacoustic image reconstruction when ground truth is unavailable, proposing a hybrid Bayesian convolutional neural network that jointly predicts images and segmentations with error estimates, showing high statistical correlation between predicted and actual errors.
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.