Review of image segmentation techniques for biomedical micro-CT: from laboratory absorption imaging to synchrotron phase-contrast

From Reconstruction to Interpretation: Zero-Setup Multi-Phase Segmentation of X-ray Tomography Data

X-ray tomography enables nondestructive characterization of material microstructures, while advances in micro-CT imaging have accelerated volumetric data acquisition and reconstruction. However, rapid interpretation remains limited by image segmentation, which often requires manual thresholding, user prompting, or material-specific model training. We present a zero-setup framework for multi-phase segmentation of synchrotron X-ray tomography data that generates interpretable masks for previously unseen datasets without user input or retraining during deployment. The framework combines a material-agnostic mask preparation strategy with a pretrained semantic segmentation network. It represents commonly occurring structural regions as background, sample, bright, dark-gray, light-gray, and porosity masks. Unlike conventional deep learning pipelines that require dataset-specific annotations and retraining, the proposed framework can be applied directly to new scans and produce diagnostic-level segmentations within minutes of reconstruction. This enables rapid assessment of scan quality, sample morphology, porosity, and attenuation variations during ongoing beamline experiments. The generated masks can later be manually refined or used to fine-tune application-specific models when greater accuracy or material-specific labeling is required. Evaluation on held-out synchrotron micro-CT images and qualitative testing on additional datasets demonstrate consistent and physically meaningful segmentations across varying samples and imaging conditions. The framework also substantially outperforms conventional intensity-based thresholding. By connecting high-speed reconstruction with immediate interpretation, the approach supports near-real-time beamline feedback and scalable AI-assisted scientific imaging workflows.

AI-based reconstruction from low-resolution acquisitions for diffusion MRI: evaluation of FOD similarity.

An Improved Pre-processing Method for High-Quality MRI Images in Brain Tumor Detection

Magnetic Resonance Imaging (MRI) is among the effective methodologies to identify tumors in the brain, but this method may not be very reliable because of the challenges in acquiring the images, which may include image noise, contrast differences, and spatial variation of intensity. To solve these problems, this study suggests an innovative pre-processing framework that will be used to improve the quality of MRI images prior to segmentation and tumor analysis. This study critically compares some noise removal methods, such as Gaussian, median, Wiener, and guided filtering, as well as Discrete Wavelet Transform (DWT)-based de-noising with soft thresholding. The new hybrid model, based on the combination of the advantages of various methods, is a Wavelet-NLM-Median (WNM). WNM integrates multiresolution wavelet shrinkage, non-local redundancy modelling, and median-based edge preservation to achieve improved noise reduction while maintaining structural details. Extensive testing is performed across noise levels ranging from 5% to 50%, and performance is assessed using standard evaluation metrics such as PSNR, MSE, SSIM, and SNR. The proposed WNM hybrid model demonstrates the highest reconstruction quality at 5% noise, with a PSNR of 45.98 dB, MSE of 3.35, SSIM of 0.985, and SNR of 44.89 dB. These statistics were substantially better than those of the Wiener and Guided filters, as well as soft-thresholding based on DWT. Visual assessments also showed that the WNM hybrid approach does a better job of keeping tumor boundaries, fine textures, and structural patterns than any other baseline filtering method. This shows that it is better at restoring high-quality MRI images for later study. The improved MRI inputs used to pre-process training images for a deep learning segmentation model improve the accuracy of the segmentation and the sharpness of the boundaries in a way that could be quantified. The suggested WNM pipeline is quick, works with many types of modalities, and is simple to connect to clinical CAD systems. It is a giant leap in pre-processing the MRI to detect malignancies in the brain.

Integrate Deep Learning and Image Processing to Improve Low-Contrast Medical Images: Advancements in Image Enhancement

Medical imaging is necessary for the diagnosis, treatment, and monitoring of sickness. Technology, background noise, and patient features can all contribute to low contrast in medical images. Diagnostic errors are caused by poor contrast, which obscures important anatomical structures and disease characteristics. To enhance lowcontrast medical images, the study employed deep learning in conjunction with conventional image processing techniques. Combining CNNs with sophisticated image enhancement techniques such as histogram equalization, contrast stretching, and noise reduction, this system produces high-quality results. Automated contrast enhancement with structural preservation is a capability of deep learning models that learn complicated visual cues. Supervised and unsupervised learning lead to better X-ray, CT, and MRI generalization. By incorporating attention processes, the model is able to better strengthen its focus on diagnostically relevant regions. Images are uniformly and artifact-free post-processed by the system. Improvements have been made to the image’s contrast, clarity, and diagnostic accuracy. PSNR and SSIM improve the quality of the image. Clinical decision-making and patient outcomes can be improved using automated, robust medical image enhancement solutions that deep learning and image processing can provide, as demonstrated in this work.

Synchrotron phase contrast micro-CT of prostate tissue

Formalin-fixed prostate tissue samples were imaged by propagation-based synchrotron phase contrast micro computed tomography (µCT) with a 3D spatial resolution of ca. 3 µm. Post-µCT, samples were prepared for histology with sections close to coplanar with the transverse µCT image planes. Haematoxylin and eosin stained sections were examined by an expert prostate histopathologist and compared qualitatively with corresponding µCT-visible microstructure features. There is potential for µCT to provide complimentary information to conventional histology and light microscopy without the need for preparation of stained thin sections. For the imaging conditions and spatial resolution of our study, µCT may provide tissue architectural features similar to those used in Gleason grading, albeit without clear subcellular microstructure detail. At the spatial resolution of our study µCT may provide novel 3D microstructure information for validation of diffusion weighted magnetic resonance imaging (MRI) methods. As an example, we demonstrate a qualitative correlation between µCT-derived stromal fibre orientation and preferential water diffusion direction measured by diffusion tensor MRI microscopy of the same sample.

A similarity-aware network with contrastive optimization for biomedical image segmentation

Accurate biomedical image segmentation is crucial for clinical diagnosis. Convolutional neural networks and Transformer-based models have been widely used for biomedical image segmentation and have improved segmentation accuracy across multiple imaging modalities. However, most existing methods rely heavily on supervised learning with large-scale annotated datasets, while professionally annotated biomedical images remain scarce. In addition, the geometric consistency available from augmented and unlabeled images is not always explicitly exploited. We proposed a similarity-aware network with contrastive optimization for biomedical image segmentation, termed SimBIS. SimBIS supplements pixel-wise supervised learning with an output-space consistency objective. Specifically, segmentation predictions generated from augmented views of the same object are projected into a seven-dimensional Hu-moment space, and the discrepancy between these projections is minimized during training. The Hu-moment is used as a parameter-free auxiliary descriptor of global mask geometry, which is invariant to translation, isotropic scaling, and in-plane rotation. Fine-grained and irregular boundaries are still optimized through the pixel-level supervised loss. SimBIS improved mDice and mIoU on both LID and SID datasets across multiple classic segmentation baselines. On publicly available biomedical segmentation tasks, including Kvasir-Seg, CVC-ClinicDB, and ISIC 2018, SimBIS not only improved the performance of the original backbone models but also outperformed other state-of-the-art methods on various metrics. Visual segmentation results further showed that SimBIS can effectively segment small targets and distinguish challenging structures, producing more accurate and consistent contours than existing models. SimBIS improves biomedical image segmentation by incorporating Hu-moment-based global shape consistency into end-to-end optimization while retaining pixel-wise supervision. It effectively leverages augmented and unlabeled data without introducing additional trainable parameters or inference-time cost, demonstrating strong potential for annotation-efficient biomedical image segmentation.