SUPER Decoder Block for Reconstruction-Aware U-Net Variants

A new segmentation pipeline utilizing a lightweight U-Net model has been developed for analyzing carbide microstructures in scanning electron microscopy (SEM) images of reactor-pressure-vessel steel. This model, trained on only 10 annotated SEM images, achieves a Dice-Sørensen coefficient of 0.98, significantly surpassing traditional methods that yield a coefficient of 0.85. The approach not only reduces annotation efforts by an order of magnitude but also generalizes to other steel types, enhancing automated carbide quantification for alloy design.

The article presents CLUE (Controllable Latent space of Unprompted Embeddings), a generative model framework designed for text-to-image synthesis. CLUE aims to generate diverse images while ensuring stability, utilizing fixed-format prompts without the need for additional data. Built on the Stable Diffusion architecture, it incorporates a Style Encoder to create style embeddings, which are processed through a new attention layer in the U-Net. This approach addresses challenges faced in specialized fields like medicine, where data is often limited.

The article discusses a novel method for unsupervised segmentation of Micro-CT scans of polyurethane structures by integrating Hidden Markov Random Fields (HMRF) with a U-Net architecture. This approach aims to enhance segmentation accuracy and speed without the need for large labeled datasets, which are typically required for supervised learning. The integration of HMRF theory with convolutional neural networks (CNNs) represents a significant advancement in the field of material property analysis.