77 research outputs found
Augmented Reality-based Feedback for Technician-in-the-loop C-arm Repositioning
Interventional C-arm imaging is crucial to percutaneous orthopedic procedures
as it enables the surgeon to monitor the progress of surgery on the anatomy
level. Minimally invasive interventions require repeated acquisition of X-ray
images from different anatomical views to verify tool placement. Achieving and
reproducing these views often comes at the cost of increased surgical time and
radiation dose to both patient and staff. This work proposes a marker-free
"technician-in-the-loop" Augmented Reality (AR) solution for C-arm
repositioning. The X-ray technician operating the C-arm interventionally is
equipped with a head-mounted display capable of recording desired C-arm poses
in 3D via an integrated infrared sensor. For C-arm repositioning to a
particular target view, the recorded C-arm pose is restored as a virtual object
and visualized in an AR environment, serving as a perceptual reference for the
technician. We conduct experiments in a setting simulating orthopedic trauma
surgery. Our proof-of-principle findings indicate that the proposed system can
decrease the 2.76 X-ray images required per desired view down to zero,
suggesting substantial reductions of radiation dose during C-arm repositioning.
The proposed AR solution is a first step towards facilitating communication
between the surgeon and the surgical staff, improving the quality of surgical
image acquisition, and enabling context-aware guidance for surgery rooms of the
future. The concept of technician-in-the-loop design will become relevant to
various interventions considering the expected advancements of sensing and
wearable computing in the near future
SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM
The Segment Anything Model (SAM) is a new image segmentation tool trained
with the largest available segmentation dataset. The model has demonstrated
that, with prompts, it can create high-quality masks for general images.
However, the performance of the model on medical images requires further
validation. To assist with the development, assessment, and application of SAM
on medical images, we introduce Segment Any Medical Model (SAMM), an extension
of SAM on 3D Slicer - an image processing and visualization software
extensively used by the medical imaging community. This open-source extension
to 3D Slicer and its demonstrations are posted on GitHub
(https://github.com/bingogome/samm). SAMM achieves 0.6-second latency of a
complete cycle and can infer image masks in nearly real-time.Comment: 5 pages, 4 figures. We added editorial changes in the tex
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