The core of the method is a two-stage pipeline. First, photographs of rockfill material—whether spread on a laboratory bench or compacted in layers on a dam site—are fed into Cellpose, a generalist deep learning algorithm originally developed for segmenting individual cells in microscopy images. Cellpose’s neural network learns to delineate the boundaries of touching, overlapping, irregularly shaped objects, which makes it unusually well suited to the visual chaos of a pile of quarried rock. The algorithm produces a high-precision segmentation mask in which every individual particle is separated from its neighbors, solving the classic bottleneck that has limited automated rock image analysis: stones that touch or partially overlap are notoriously difficult for conventional thresholding techniques to separate.

Once Cellpose has produced the segmented image, the researchers turn to the MorphoLibJ plugin of ImageJ, a mature mathematical morphology library, for post-processing and quantitative computation. From each segmented particle, the software extracts geometric features including area, particle size, and aspect ratio. Aggregated over the hundreds or thousands of stones visible in a single image, these features yield the particle size distribution curve—one of the most fundamental descriptors of any granular construction material—without the sieving, weighing, and labor that traditional gradation testing demands. The team reports that the ImageJ-Cellpose method could accurately determine both the particle size distribution curve and the porosity of rockfill materials, results that were validated against conventional screening and porosity experiments conducted in the laboratory.

Beyond reproducing established metrics, the study introduces new shape parameters for rockfill material, including what the authors call the long-short ratio and the area ratio. Shape matters in geotechnical engineering more than casual observers might expect: angular, elongated, and flat particles interlock differently than rounded, equidimensional ones, influencing shear strength, particle breakage under load, and the volumetric behavior of the compacted mass. By statistically analyzing the distribution of these new shape descriptors across particle populations, the researchers offer a quantitative framework for describing rockfill morphology that goes beyond simple size gradation, opening a path toward correlating shape statistics directly with mechanical performance.

The critical question for any laboratory method is whether it survives contact with the real world. To answer it, the team partnered with a pumped-storage power station, where layered rolling experiments were carried out during actual rockfill placement. As the material was compacted by rollers in successive passes, images were collected at each stage and processed through the intelligent recognition pipeline. The results showed that the method could effectively trace how the particle size distribution and porosity of the rockfill evolved with each rolling pass—precisely the kind of real-time feedback that compaction quality control has historically lacked. Encouragingly, the trends identified by the image-based approach aligned well with those derived from the pit measuring method, the traditional but destructive and labor-intensive standard for evaluating compaction in the field.

The significance of this validation lies in what it could change about dam construction practice. Rockfill dams are among the largest man-made structures on Earth, and their long-term safety depends on how well the fill is compacted during construction; poor compaction leads to post-construction settlement, cracking of upstream concrete face slabs, and heightened vulnerability to overtopping failure and seismic loading. Current quality control typically relies on periodic pit sampling, in which crews excavate a hole, weigh the removed material, and measure density—a slow process that samples only a tiny fraction of the dam’s volume. A camera, a laptop running Cellpose and ImageJ, and the proposed analytical workflow could in principle assess compaction indicators continuously and across the entire working surface, converting construction monitoring from intermittent spot checks into comprehensive surveillance.

Technically, the study is notable for transplanting tools from an unexpected domain. Cellpose was created by biologists to solve the problem of segmenting cells in diverse image types without retraining a network for each new dataset, and its generalist design proved transferable to geological imagery. ImageJ, likewise, grew out of biomedical imaging but ships with general-purpose morphology operators that apply equally well to mineral particles. By connecting a state-of-the-art segmentation network to a battle-tested analysis library, the Xi’an team sidestepped the need to build bespoke deep learning infrastructure or large annotated rockfill datasets from scratch, lowering the barrier to adoption for engineering firms and construction supervisors who cannot maintain their own machine learning teams.

The method also complements a growing body of work on computer vision in geotechnical monitoring. Prior studies have used video image recognition to detect rockfill gradation, deep learning segmentation to calculate gradation from photographs, and instance segmentation to predict gradation for engineering projects. What distinguishes the new approach is its end-to-end coverage: rather than estimating only particle sizes, it simultaneously delivers size distribution, individual particle shape metrics through newly proposed parameters, and porosity, all from ordinary images, and it does so in a form validated against both sieving tests in the laboratory and pit measurements on an active construction site. The authors argue that this makes the technique suitable for real-world engineering applications and a robust means of ascertaining particle size information during the construction of rockfill dams.

Looking forward, the research points toward a swift and precise evaluation method for the compaction quality of rockfill material—a capability the authors describe as paramount for the comprehensive understanding and analysis of the safety conditions of rockfill dams and similar projects. If deployed at scale, the ImageJ-Cellpose workflow could give dam engineers a continuous, quantitative picture of how each roller pass is reshaping the granular skeleton of an embankment, catch gradation or compaction anomalies before they are buried under the next lift of rock, and feed high-quality geometric data into the numerical models used to predict settlement, seepage, and seismic response. In an era when aging dam infrastructure faces intensifying climate and seismic stresses, the idea that a deep learning algorithm designed for counting cells can help safeguard structures holding back billions of liters of water is a striking demonstration of how tools migrate across scientific boundaries—and of how much of modern construction safety may soon rest on image recognition pipelines that work quietly, frame by frame, on the rocks beneath the rollers.

Subject of Research: Intelligent image-based geometric characterization of rockfill materials using deep learning segmentation for dam compaction analysis

Article Title: Intelligent geometric characterization of rockfill materials using ImageJ-Cellpose: a novel approach for accurate morphological analysis

Article References: Ma, C., Zhou, C., Hou, Y., & Cheng, L. (2026). Intelligent geometric characterization of rockfill materials using ImageJ-Cellpose: a novel approach for accurate morphological analysis. Cluster Computing, 29(13), Article 779. https://doi.org/10.1007/s10586-026-06480-4

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06480-4

Keywords: rockfill materials, deep learning, Cellpose, ImageJ, image segmentation, particle size distribution, porosity, particle shape, compaction quality, rockfill dams, image recognition, geotechnical engineering

The study, published in JAMA Dermatology, centers on a technology known as line-field confocal optical coherence tomography, or LC-OCT. This imaging platform merges two established techniques into a single device. The first is optical coherence tomography, commonly abbreviated OCT, which uses low-coherence light to capture cross-sectional images of tissue and provides reliable information about the thickness and lateral extent of a tumor. The second is line-field confocal microscopy, which delivers sharply resolved images at the cellular level, allowing clinicians to examine individual cell structures within the upper layers of the skin without taking a biopsy.

The combination is what gives LC-OCT its unusual diagnostic power. Whereas OCT alone offers depth but limited cellular detail, and confocal microscopy alone offers cellular detail but limited depth, the hybrid technique produces vertical, horizontal and fully three-dimensional images of the skin in real time, with a resolution on the order of micrometers, that is, thousandths of a millimeter. For perspective, a single human hair is roughly 50 micrometers thick. The result is a live, non-invasive window into the architecture of the skin that approaches what a pathologist would see under a microscope, but without cutting, staining or waiting for laboratory processing.

LC-OCT is already in clinical use as a standard tool for detecting basal cell carcinoma in its early stages and for monitoring how well non-invasive therapies are working. What the FAU team has added is a layer of artificial intelligence trained to interpret the images. The AI analyzes the optical data as it is acquired and overlays a color-coded probability value onto the image, indicating the likelihood that basal cell carcinoma is present in each region examined. This allows the physician to recognize suspicious areas at a glance, dramatically reducing the cognitive load of scanning large or subtle lesions frame by frame.

Importantly, the researchers emphasize that the algorithm does not replace clinical judgment. It is still the physician who makes the final diagnosis, weighing the AI-assisted image alongside the patient’s history, the appearance of the lesion and, where necessary, a conventional biopsy. The technology is best understood as a decision-support system: it directs attention to areas that merit closer scrutiny and can help distinguish malignant tissue from benign look-alikes, but accountability for the diagnostic decision remains with the treating clinician.

The clinical stakes of earlier detection are considerable. Basal cell carcinoma is a locally aggressive tumor: it spreads within the skin in its vicinity and destroys the tissue it invades, which can damage delicate structures such as the nose or the eyes when it arises on the face. Although it rarely metastasizes, its capacity for local destruction makes timely intervention essential. As Dr. Moritz Ronicke of FAU notes, detecting basal cell carcinoma early allows for less invasive treatment options, and in some cases all that is required is a medicated cream rather than surgical excision. Earlier and more confident diagnosis therefore translates directly into gentler treatment, better cosmetic outcomes and reduced burden on both patients and health systems.

The truly forward-looking element of the FAU work is a concept the researchers call SUBSCAN. Rather than imaging a lesion that has already attracted clinical suspicion, SUBSCAN would extend LC-OCT screening to people who carry a high risk of developing skin cancer but who do not yet have a visible tumor. In principle, the AI-assisted imaging could identify the microscopic signatures of basal cell carcinoma forming beneath an apparently normal skin surface, enabling a diagnosis even earlier in the disease process than current practice allows. If such risk-stratified optical screening were routinely adopted, Ronicke suggests, it could lead to more widespread use of topical creams for treating basal cell carcinoma, in some cases avoiding the need for surgery altogether.

That vision, however, is not yet ready for the clinic. The researchers are careful to point out that SUBSCAN cannot currently be offered routinely, for two principal reasons. First, there is still a lack of data on the sensitivity of the method, meaning it is not yet established how reliably the technique detects all early tumors and how often it might produce false alarms when applied to unselected high-risk skin. Second, the examination remains too time-consuming for routine deployment in a busy dermatology practice. Large prospective studies will be needed to quantify diagnostic performance, streamline the imaging workflow and define precisely which patient groups would benefit most from preventive scanning.

Even so, the trajectory of the technology is striking. Dermatology has been moving for years toward non-invasive diagnostics, from dermoscopy to reflectance confocal microscopy to OCT, each generation trading some simplicity for a deeper and sharper view of living skin. LC-OCT with AI-assisted image recognition represents a convergence of that trend: micrometer-scale, three-dimensional, real-time imaging paired with machine learning that distills complex optical data into an immediately interpretable probability map. For patients at elevated risk of skin cancer, the prospect of a diagnosis delivered before a tumor ever becomes visible, and of treatment with a simple cream instead of a scalpel, marks a compelling glimpse of where early cancer detection may be heading.

Subject of Research: AI-assisted line-field confocal optical coherence tomography for early detection of basal cell carcinoma

Article Title: Detecting skin cancer even before it becomes visible

Article References: Detecting skin cancer even before it becomes visible. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: basal cell carcinoma, skin cancer, LC-OCT, optical coherence tomography, confocal microscopy, artificial intelligence, image recognition, early detection, non-invasive diagnosis, dermatology, JAMA Dermatology, SUBSCAN