A team of biomedical engineers in Halifax, Canada, has demonstrated that a low-cost, handheld 3D ultrasound system can map the contours of the femoral head and neck with sub-millimetre accuracy, matching the performance of computed tomography (CT) for the key measurements clinicians use to diagnose a common and painful hip condition. The work, led by Andrew D. Michels, Orion P. C. Wiersma, Grace Yu, Aratha Thanamayooran, and Robert B. A. Adamson of Dalhousie University’s School of Biomedical Engineering, together with orthopaedic surgeon Ivan Wong of Nova Scotia Health, was published in the International Journal of Computer Assisted Radiology and Surgery and could reshape how femoroacetabular impingement syndrome (FAIS) is diagnosed, monitored, and surgically planned.
FAIS is a disorder in which abnormal bony contact between the femoral head-and-neck junction and the acetabulum, the socket of the hip joint, causes pain, cartilage damage, and, in many patients, early-onset osteoarthritis. The most common bony culprit is the so-called cam deformity, a bulge of excess bone at the anterolateral head-neck junction that alters the normally spherical profile of the femoral head. Detecting and quantifying that deformity currently depends on imaging: plain radiographs provide a first look, while CT and magnetic resonance imaging offer the three-dimensional detail needed to characterise the shape and location of the bump and to guide arthroscopic or open surgical correction. But CT exposes young patients, who often make up the FAIS population, to ionising radiation, and MRI is expensive and poorly suited to capturing cortical bone surfaces directly.
The Dalhousie team’s alternative is a freehand 3D ultrasound system, a class of technology in which a conventional two-dimensional ultrasound probe is swept across the skin while its position and orientation in space are tracked continuously. Each B-mode frame, tagged with its pose, becomes a slice in a virtual volume that software can later reconstruct into a three-dimensional representation of the underlying anatomy. What has historically limited this approach for bone imaging is segmentation: bone appears in ultrasound as a bright, often incomplete hyperechoic line with shadowing beneath it, and manually delineating those surfaces across thousands of frames is impractical in a clinical setting. The new system solves this bottleneck with deep learning.
The researchers paired an off-the-shelf optical tracking system with a low-cost point-of-care ultrasound probe. A feature pyramid network, a convolutional neural network architecture originally developed for object detection, was trained to segment bone surfaces from B-mode images. Critically, rather than building a training set from scratch, the team began with a publicly available large-scale dataset of ultrasound bone images, and then refined the model through transfer learning using a smaller set of curated, manually segmented images specific to their application. Transfer learning allows knowledge gained on one large, general dataset to be repurposed for a narrower task with far less labelled data, dramatically reducing the labour of building a clinically deployable segmentation model.
From the segmented bone lines in each tracked frame, deterministic algorithms assembled point clouds of the femoral head and neck surfaces. These were then converted into watertight surface meshes, transforming a collection of partial, overlapping ultrasound sweeps into a coherent three-dimensional model of the bone. The entire pipeline, from probe to mesh, was designed around inexpensive, accessible hardware and open-source components, a deliberate choice that lowers the barrier to adoption compared with specialised, high-cost imaging platforms.
To validate the system, the team compared ultrasound-derived bone surfaces against CT reconstructions in four cadaveric hips and two patient hips. The results were striking: geometric agreement with CT, as well as within-subject repeatability and within-subject reproducibility, all fell in the range of 0.4 to 0.6 millimetres. Those figures matter because the clinically significant bone deformities associated with FAIS measure two millimetres or more. A system that deviates from CT by less than half that threshold can reliably resolve the bumps and asphericities that surgeons need to see. Repeatability and reproducibility matter equally, since a diagnostic tool that returns different answers when the same hip is scanned twice, or scanned by different operators, cannot support confident clinical decisions.
The team also tested the system on the measurement that dominates FAIS assessment: the alpha angle. The alpha angle quantifies the head-neck asphericity by measuring, in a defined plane, the angle between the axis of the femoral neck and the line from the centre of the femoral head to the point where the bony contour departs from a circle. Values above roughly 55 to 60 degrees are conventionally associated with cam impingement, and clinically meaningful differences between measurements exceed five degrees. In the validation experiments, alpha angles derived from the ultrasound reconstructions agreed with those derived from CT to within 0.6 degrees, a margin of error an order of magnitude smaller than the clinically relevant threshold.
The group then moved beyond cadavers and patients already undergoing imaging. Five healthy volunteers underwent scanning to assess feasibility in a live clinical environment, and the system successfully produced three-dimensional bone surface maps of the femoral head in vivo. The demonstration suggests the workflow, sweep the probe, let the network segment the bone, reconstruct the mesh, compute the metrics, is practical for real patients rather than only in controlled laboratory conditions. A supplementary video accompanying the publication illustrates the system in operation.
The implications extend beyond FAIS. Freehand 3D ultrasound has been explored for decades in applications ranging from spinal imaging to foetal biometry and liver interventions, but bone has remained one of the hardest targets because of the physics of ultrasound-tissue interaction. By combining modern semantic segmentation networks, public training datasets, transfer learning, optical tracking, and robust surface reconstruction, the Dalhousie study provides a template that other groups could adapt for osteoarthritis assessment, fracture evaluation, pediatric hip monitoring, and intraoperative guidance, all without radiation. For orthopaedic surgeons, the prospect of quantifying a cam deformity in the clinic, at the point of care, with a probe and a tracker rather than a CT scanner, represents a meaningful shift in diagnostic economics and accessibility.
The authors are careful to frame the work as validation rather than deployment; the study sample was small, comprising four cadaveric hips, two patient hips, and five volunteers, and broader clinical adoption will require larger multicentre studies, demonstration of performance across diverse patient anatomies and body habitus, and integration into existing orthopaedic workflows. The system’s reliance on optical tracking also imposes practical constraints: the probe must remain within the tracker’s field of view during a sweep, which shapes how operators scan the hip. Nevertheless, the combination of 0.4 to 0.6 millimetre accuracy, 0.6 degree agreement on the alpha angle, and successful in vivo imaging meets or exceeds every performance threshold the authors set out as clinically necessary. As the researchers conclude in the paper, the system as demonstrated is suitable for clinical deployment, and the path from laboratory prototype to a radiation-free, affordable tool for hip morphometry has never looked shorter.
Subject of Research: A freehand 3D ultrasound system with deep learning-based bone segmentation for assessing femoral head and neck deformities in femoroacetabular impingement syndrome
Subject of Research: Medicine
Article Title: Freehand 3D ultrasound imaging of the femoral head and neck
Article References: Michels, A. D., Wiersma, O. P. C., Yu, G., Thanamayooran, A., Wong, I., & Adamson, R. B. A. (2026). Freehand 3D ultrasound imaging of the femoral head and neck. International Journal of Computer Assisted Radiology and Surgery. https://doi.org/10.1007/s11548-026-03762-5
Image Credits: AI Generated
DOI: 10.1007/s11548-026-03762-5
Keywords: ultrasound, 3D imaging, freehand, femoroacetabular impingement, semantic segmentation, transfer learning, imaging, femoral head, deep learning
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Ophelia Keating. (September 9, 2026). Researchers develop freehand 3D ultrasound for imaging hip bones. Scienmag. https://scienmag.com/researchers-develop-freehand-3d-ultrasound-for-imaging-hip-bones/
Ophelia Keating. “Researchers develop freehand 3D ultrasound for imaging hip bones.” Scienmag, 9 September 2026, https://scienmag.com/researchers-develop-freehand-3d-ultrasound-for-imaging-hip-bones/. Accessed 9 September 2026.
Ophelia Keating. “Researchers develop freehand 3D ultrasound for imaging hip bones.” Scienmag. September 9, 2026. https://scienmag.com/researchers-develop-freehand-3d-ultrasound-for-imaging-hip-bones/
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