The study, published in the International Journal of Computer Assisted Radiology and Surgery by researchers from Eindhoven University of Technology and Catharina Hospital, tackles a problem that has long frustrated radiologists. When they examine CT scans for peritoneal metastases, subtle lesions are easily missed, and the extent of disease is frequently underestimated compared with what a surgeon actually finds. Part of the difficulty is structural: the surgical PCI system, developed by Sugarbaker, was never designed for cross-sectional imaging, so radiologists assessing the abdomen on CT have lacked standardized regional boundaries. The result is inconsistent reporting and considerable variability between readers, which may explain why only a minority of radiologists currently attempt PCI scoring on images at all.

A recent Delphi consensus study involving 88 international experts in radiology, surgery, and gynecology changed that landscape by defining thirteen radiological PCI regions, numbered 0 through 12: the central region, right and left upper abdomen, epigastrium, left and right flanks, left and right lower abdomen, pelvis, and four small-bowel regions covering the upper and lower jejunum and ileum. The new research takes those expert-defined boundaries and teaches an artificial intelligence to reproduce them automatically. Crucially, the regions are not organ segmentations; they are subdivisions of the peritoneal cavity itself, each bounded by anatomical landmarks and surrounding organ surfaces—a spatial partition that is far harder for a neural network to learn than, say, the outline of a liver.

To train and test their models, the team assembled 62 contrast-enhanced CT scans from patients with gastric, ovarian, and colorectal cancers, deliberately spanning the full PCI range from 0 to 39. Every scan was painstakingly annotated in three dimensions by clinical researchers, with each annotation reviewed by a second annotator and disagreements settled by an expert radiologist. The effort involved was enormous: annotating a single scan takes roughly five hours. The researchers then compared two of the most widely used architectures in 3D medical image segmentation—the self-configuring convolutional network nnU-Net and the transformer-based Swin UNETR—using fivefold cross-validation and standard metrics including the Dice similarity coefficient, the 95th-percentile Hausdorff distance, and average surface distance.

The head-to-head comparison delivered a clear verdict. The convolutional nnU-Net achieved an overall Dice score of 0.81, outperforming the transformer-based Swin UNETR, which reached 0.76. Beyond raw accuracy, nnU-Net proved markedly more stable across patients, with a higher median Dice in every single region, whereas the transformer showed greater sensitivity to patient-specific anatomical variability. Both models struggled most in the right flank and the small-bowel regions, where regional boundaries are defined geometrically rather than by visible tissue contrast—precisely the places where a network trained purely on image appearance has nothing to latch onto.

That observation inspired the study’s most inventive contribution: an anatomically constrained pipeline that sidesteps the need to learn ill-defined boundaries at all. Instead of asking the network to distinguish, for example, the upper jejunum from the lower jejunum on a CT image where no visible border exists, the researchers trained a second nnU-Net on merged super-regions—grouping the flank and lower regions on each side, and all four small-bowel regions together. After prediction, a deterministic post-processing step splits these super-regions back into the thirteen individual regions using anatomical landmarks detected by the established TotalSegmentator tool. A transverse plane at the top of each hip bone divides flank from lower abdomen, while anteroposterior planes radiating from the ligament of Treitz—the duodenum–small-bowel transition—carve the small-bowel super-region into four equal-volume wedges, mirroring the consensus definitions.

The results were striking. The anatomically constrained pipeline lifted the overall Dice score from 0.81 to 0.84 and reduced boundary errors substantially, with the 95th-percentile Hausdorff distance falling from 13.7 to 11.8 millimeters and average surface distance from 4.1 to 3.4 millimeters. The largest gains came exactly where the baseline had faltered: in the small-bowel regions, mean Dice improved from 0.74 to 0.82, and average surface distance dropped from 5.8 to 3.7 millimeters. Residual errors in these regions were traced mainly to the localization of the anatomical landmarks themselves rather than to the segmentation network, pointing to a clear target for future refinement.

Perhaps the most clinically meaningful comparison in the study is not model against model but machine against human. On a separate subset of ten CT scans, each independently annotated in triplicate by three clinical researchers, the interobserver Dice score—the agreement between human experts—was 0.87. The automated pipeline, evaluated on identical data, achieved 0.84, with boundary-error metrics of the same order of magnitude as human variability. In most regions, the model’s agreement with human annotators approached the level at which humans agree with one another, and in the small-bowel regions the model actually showed far less variation across patients than the human observers did. In other words, the algorithm is already performing near the ceiling set by human consistency itself.

The clinical implications extend beyond a single score. Because current radiological assessment of peritoneal disease lacks standardized regional boundaries, an automatic segmentation overlay could guide radiologists to systematically inspect each predefined region, reduce interobserver variability, standardize reporting, and support surgical planning. It could also accelerate reporting workflows and serve as a training aid for less experienced readers. Computationally, the approach is feasible for real-world use: nnU-Net inference takes roughly seven seconds per scan on a single NVIDIA A100 GPU. The researchers envision a human-in-the-loop workflow in which the model proposes regions and radiologists make minor boundary adjustments, and a longer-term extension in which lesions are localized within each region and mapped to PCI subscores automatically.

The authors are candid about the limitations. The study is single-center, with a private dataset of 62 scans, and variation in scanners, acquisition protocols, and contrast timing across institutions could affect generalizability; external validation on multi-center data is a stated priority. The interobserver analysis rested on only ten scans, because triple-annotating the full cohort at five hours per scan was infeasible. Some patients with surgically altered anatomy or large tumors that displace organs produced outlier errors, particularly affecting the rule-based post-processing in the right flank. Yet the direction of travel is unmistakable. With code and trained model weights released openly on GitHub, and the radiological PCI framework itself born from international consensus, the study offers a reproducible foundation for something oncology has wanted for decades: an objective, noninvasive, and standardized measure of peritoneal cancer burden that any hospital with a CT scanner could one day compute in seconds.

Subject of Research: Deep learning segmentation of radiological peritoneal cancer index regions on CT for noninvasive staging of peritoneal metastases

Article Title: Deep learning-based segmentation of peritoneal cancer index regions from CT imaging

Article References: Gort, P. C., Fleurkens-Ewals, L. J. S., Kampmeijer, L. D., van Herwijnen, A. F., Tops-Welten, M. W., Claessens, C. H. B., Nederend, J., De Hingh, I. H. J. T., Lahaye, M. J., Luyer, M. D. P., & van der Sommen, F. (2026). Deep learning-based segmentation of peritoneal cancer index regions from CT imaging. International Journal of Computer Assisted Radiology and Surgery. https://doi.org/10.1007/s11548-026-03796-9

Image Credits: AI Generated

DOI: 10.1007/s11548-026-03796-9

Keywords: peritoneal metastases, peritoneal cancer index, deep learning, CT imaging, medical image segmentation, nnU-Net, Swin UNETR, TotalSegmentator, radiology, surgical oncology, artificial intelligence, computer-assisted surgery

The research, led by Sanaa Amina Gourine of the Nara Institute of Science and Technology together with colleagues from Miyazaki University, Shiga University of Medical Science, Osaka University, and Ehime University, describes a pipeline for volumetric segmentation and quantitative analysis of 21 torso structures in CT images. Segmentation, in this context, means teaching an algorithm to trace the exact boundaries of each muscle and fat compartment voxel by voxel across a three-dimensional scan. Once those boundaries are known, software can compute each structure’s volume in cubic centimeters and its mean density in Hounsfield units, the standard radiological measure of tissue composition. Lower muscle density on CT reflects fatty infiltration of the muscle, a hallmark of aging and disuse that correlates with reduced strength and physical performance. Extracting these numbers automatically from thousands of existing scans would turn routine clinical imaging into an opportunistic screening resource for conditions that currently go undiagnosed.

The technical heart of the study is a comparison between two deep learning architectures, both augmented with a mechanism for estimating predictive uncertainty. The first is a two-dimensional Bayesian UNet, a convolutional neural network that processes the scan slice by slice and uses a technique called Monte Carlo dropout sampling. Instead of producing a single deterministic answer, the network runs its prediction multiple times with randomly deactivated internal units, and the variability across those runs serves as a proxy for how confident the model is at each location. The second, and ultimately the winning, approach is a fully three-dimensional, patch-based model built on the well-known nnU-Net framework, which the authors call the 3D MSKSegmenter. Rather than relying on repeated stochastic sampling, this model derives uncertainty directly from the entropy of its prediction probabilities: when the network’s output probabilities are spread evenly across competing classes, entropy is high and the prediction is flagged as unreliable.

That uncertainty estimate is not a mathematical flourish; it is the framework’s quality control system. In the validation experiments, the researchers found strong negative correlations between predictive uncertainty and segmentation accuracy, measured by the Dice coefficient, the standard overlap metric comparing automated contours against manual ground truth. The correlation reached minus 0.870 for the 3D MSKSegmenter and an even steeper minus 0.955 for the two-dimensional Bayesian UNet. In practical terms, when the model reports low uncertainty, its segmentation is almost always accurate, and when uncertainty climbs, accuracy tends to collapse. This relationship allows the framework to act as its own inspector: for large-scale database studies where no expert can review every output, the system can automatically filter out cases whose uncertainty exceeds a threshold, ensuring that only reliable measurements flow into downstream statistical analyses. The authors validated this quality control mechanism across a fivefold cross-validation experiment on 20 manually labeled CT images.

The 3D MSKSegmenter outperformed both its two-dimensional counterpart and a formidable external benchmark. The team compared their results against TotalSegmentator, a widely used, state-of-the-art open-source model capable of segmenting 104 anatomical structures across the whole body. Despite that model’s breadth, the torso-specialized 3D MSKSegmenter achieved a superior average Dice coefficient of 0.89 plus or minus 0.03 across the 21 target structures, a statistically significant advantage over both competitors at p less than 0.001. The result underscores a recurring lesson in medical image analysis: models trained specifically on the structures and imaging characteristics of a target anatomy, with careful handling of the enormous variability in body habitus, scan protocol, and contrast phase found in real hospital databases, can beat generalist systems on their home turf.

Robustness was then tested at scale. The researchers applied the framework to a single-institution database of approximately 1,200 patients, an external test set far larger than the labeled training and validation data. The framework proved stable across this heterogeneous clinical population, and the uncertainty-based filtering continued to identify the cases most likely to contain segmentation errors. This matters because the ultimate ambition of the project is not to segment 1,200 scans but to enable population-scale studies in which tens of thousands of torso CTs, already acquired for other reasons, are mined for musculoskeletal biomarkers without any additional imaging cost or radiation exposure. Opportunistic screening of this kind has been proposed for cardiometabolic risk and sarcopenia, but it has been limited by the absence of segmentation tools that are simultaneously accurate across many muscle groups and honest about their own failure modes.

The downstream analysis delivered the kind of biological signal that justifies the engineering effort. Measuring muscle volume and mean Hounsfield units across the large database, the researchers detected significant age- and sex-related trends in muscle density and volume, consistent with the known epidemiology of sarcopenia and age-related fatty muscle degeneration. Because the framework quantifies 19 individual muscles rather than a single aggregate muscle area at one reference slice, it opens the door to site-specific analyses: whether the gluteal muscles, the paraspinal stabilizers, or the abdominal wall degrade at different rates, and how those patterns relate to conditions such as hip osteoarthritis, knee osteoarthritis, or postoperative physical function. Prior work by overlapping research groups has linked gluteal muscle volume and quality to early recovery after total hip arthroplasty and lower limb muscle degeneration to knee osteoarthritis severity, and a scalable torso-wide tool provides the missing population-level instrument for such investigations.

The study also situates itself within a rapidly maturing field. Earlier approaches to automated muscle segmentation relied on random forest classifiers, atlas-based registration, or fuzzy c-means clustering, typically targeting one or a few muscle groups. The rise of deep learning brought UNet-style architectures and, more recently, the self-configuring nnU-Net, which has become a de facto standard in biomedical segmentation. Bayesian extensions of the UNet introduced calibrated confidence estimation to medical imaging, and previous validations demonstrated that uncertainty estimation works for bone and muscle assessment in hip-to-knee CT. The present work extends that lineage in two directions: it covers an unusually comprehensive set of torso musculature and fat compartments, and it explicitly integrates uncertainty as a gatekeeper for biomarker extraction in very large databases, rather than treating it as a laboratory curiosity.

Transparency is part of the package. The code for the 3D MSKSegmenter, built on the open nnU-Net repository, has been released on GitHub by the NAIST Bio-Image Intelligence group, and the trained models are available from the corresponding authors on reasonable request. The retrospective study was approved by local ethics committees and conducted in accordance with the Helsinki Declaration, with informed consent obtained through opt-out mechanisms. Funding came from the Japan Society for the Promotion of Science, the Japan Agency for Medical Research and Development, and the Cross-ministerial Strategic Innovation Promotion Program on integrated health care systems.

The broader implication is that the raw material for a new generation of musculoskeletal epidemiology is already sitting on hospital servers. As populations age, sarcopenia and related muscle disorders are projected to become a major driver of disability and health care cost, yet muscle status is rarely measured outside specialized research settings. A framework that can sweep through existing CT archives, quantify 21 body composition structures per patient, and flag its own uncertain outputs for review turns routine diagnostic imaging into a longitudinal window on the aging musculoskeletal system. If external validations at additional institutions replicate the accuracy and reliability reported here, the humble torso CT, acquired for everything from lung cancer screening to abdominal pain, could quietly become one of the most valuable population health instruments in modern medicine.

Subject of Research: Automated deep learning segmentation and uncertainty-based quality control for volumetric musculoskeletal analysis of torso CT images

Article Title: Automated framework for volumetric musculoskeletal segmentation and assessment of torso CT images in large-scale databases

Article References: Gourine, S. A., Soufi, M., Otake, Y., Masaki, Y., Murakami, Y., Nagatani, Y., Watanabe, Y., Uemura, K., Kono, K., Takao, M., Fujimori, T., Sugano, N., & Sato, Y. (2026). Automated framework for volumetric musculoskeletal segmentation and assessment of torso CT images in large-scale databases. International Journal of Computer Assisted Radiology and Surgery. https://doi.org/10.1007/s11548-026-03785-y

Image Credits: AI Generated

DOI: 10.1007/s11548-026-03785-y

Keywords: musculoskeletal segmentation, torso CT, deep learning, nnU-Net, predictive uncertainty, Bayesian UNet, sarcopenia, body composition, TotalSegmentator, Hounsfield units, large-scale databases, opportunistic screening