In an era of rapid advancements in X-ray physics and the growing capabilities of X-ray synchrotron sources, the analysis of tomographic X-ray datasets has become increasingly critical in various scientific, medical, and industrial applications. However, once the raw data are collected, manually segmenting these images is a time-consuming and error-prone process, often plagued by uncertainties and subjectivity. Automating the segmentation process becomes imperative to keep pace with data acquisition rates and to ensure timely scientific discoveries and industrial insights. To address this challenge, in this project, we plan to leverage the power of deep neural networks to automate the segmentation of ptychographic X-ray images, removing the need for human intervention and significantly expediting the analysis process. The primary objective is the development and training of a deep neural network, based on existing architectures, commonly used for other computer vision tasks (e.g. UNet, VGGnet, etc.) The training dataset consists of real-world X-ray images (raw and segmented), and the results will be benchmarked against manually labeled datasets.
We trained 2 models with and without data augmentation, and below are the results
