In tomography, reconstruction and analysis is often performed once the acquisition has been completed due to the computational cost of the 3D imaging algorithms. In contrast, real-time reconstruction and analysis can avoid costly repetition of experiments and enable optimization of experimental parameters. Recently, it was shown that by reconstructing a subset of arbitrarily oriented slices, real-time quasi-3D reconstruction can be attained. Here, we extend this approach by including realtime segmentation, thereby enabling real-time analysis during the experiment. We propose to use a convolutional neural network (CNN) to perform real-time image segmentation and introduce an adapted training strategy in order to apply CNNs to arbitrarily oriented slices. We evaluate our method on both simulated and real-world data. The experiments show that our approach enables realtime tomographic segmentation for real-world applications and outperforms standard unsupervised segmentation methods.

Tomography, Machine learning, Segmentation
doi.org/10.1109/MLSP49062.2020.9231642
Real-Time 3D Tomography
IEEE International Workshop on Machine Learning for Signal Processing
Computational Imaging

Schoonhoven, R.A, Buurlage, J, Pelt, D.M, & Batenburg, K.J. (2020). Real-time segmentation for tomographic imaging. In IEEE 30th International Workshop on Machine Learning for Signal Processing (pp. 1–6). doi:10.1109/MLSP49062.2020.9231642