2,826 research outputs found
Simple Muscle Architecture Analysis (SMA): an ImageJ macro tool to automate measurements in B-mode ultrasound scans
In vivo measurements of muscle architecture (i.e. the spatial arrangement of
muscle fascicles) are routinely included in research and clinical settings to
monitor muscle structure, function and plasticity. However, in most cases such
measurements are performed manually, and more reliable and time-efficient
automated methods are either lacking completely, or are inaccessible to those
without expertise in image analysis. In this work, we propose an ImageJ script
to automate the entire analysis process of muscle architecture in ultrasound
images: Simple Muscle Architecture Analysis (SMA). Images are filtered in the
spatial and frequency domains with built-in commands and external plugins to
highlight aponeuroses and fascicles. Fascicle dominant orientation is then
computed in regions of interest using the OrientationJ plugin. Bland-Altman
plots of analyses performed manually or with SMA indicates that the automated
analysis does not induce any systematic bias and that both methods agree
equally through the range of measurements. Our test results illustrate the
suitability of SMA to analyse images from superficial muscles acquired with a
broad range of ultrasound settings.Comment: 8 pages, 7 figures, 1 appendi
Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback
Ultrasound (US) imaging is widely employed for diagnosis and staging of
peripheral vascular diseases (PVD), mainly due to its high availability and the
fact it does not emit radiation. However, high inter-operator variability and a
lack of repeatability of US image acquisition hinder the implementation of
extensive screening programs. To address this challenge, we propose an
end-to-end workflow for automatic robotic US screening of tubular structures
using only the real-time US imaging feedback. We first train a U-Net for
real-time segmentation of the vascular structure from cross-sectional US
images. Then, we represent the detected vascular structure as a 3D point cloud
and use it to estimate the longitudinal axis of the target tubular structure
and its mean radius by solving a constrained non-linear optimization problem.
Iterating the previous processes, the US probe is automatically aligned to the
orientation normal to the target tubular tissue and adjusted online to center
the tracked tissue based on the spatial calibration. The real-time segmentation
result is evaluated both on a phantom and in-vivo on brachial arteries of
volunteers. In addition, the whole process is validated both in simulation and
physical phantoms. The mean absolute radius error and orientation error (
SD) in the simulation are and ,
respectively. On a gel phantom, these errors are and
. This shows that the method is able to automatically screen
tubular tissues with an optimal probe orientation (i.e. normal to the vessel)
and at the same to accurately estimate the mean radius, both in real-time.Comment: Accepted for publication in IEEE Transactions on Industrial
Electronics Video: https://www.youtube.com/watch?v=VAaNZL0I5i
Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms
Currently, diagnosis of skin diseases is based primarily on visual pattern
recognition skills and expertise of the physician observing the lesion. Even
though dermatologists are trained to recognize patterns of morphology, it is
still a subjective visual assessment. Tools for automated pattern recognition
can provide objective information to support clinical decision-making.
Noninvasive skin imaging techniques provide complementary information to the
clinician. In recent years, optical coherence tomography has become a powerful
skin imaging technique. According to specific functional needs, skin
architecture varies across different parts of the body, as do the textural
characteristics in OCT images. There is, therefore, a critical need to
systematically analyze OCT images from different body sites, to identify their
significant qualitative and quantitative differences. Sixty-three optical and
textural features extracted from OCT images of healthy and diseased skin are
analyzed and in conjunction with decision-theoretic approaches used to create
computational models of the diseases. We demonstrate that these models provide
objective information to the clinician to assist in the diagnosis of
abnormalities of cutaneous microstructure, and hence, aid in the determination
of treatment. Specifically, we demonstrate the performance of this methodology
on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)
from healthy tissue
Locally rigid, vessel-based registration for laparoscopic liver surgery
Purpose: Laparoscopic liver resection has significant advantages over open surgery due to less patient trauma and faster recovery times, yet is difficult for most lesions due to the restricted field of view and lack of haptic feedback. Image guidance provides a potential solution but is challenging in a soft deforming organ such as the liver. In this paper, we therefore propose a laparoscopic ultrasound (LUS) image guidance system and study the feasibility of a locally rigid registration for laparoscopic liver surgery.
Methods: We developed a real-time segmentation method to extract vessel centre points from calibrated, freehand, electromagnetically tracked, 2D LUS images. Using landmark-based initial registration and an optional iterative closest point (ICP) point-to-line registration, a vessel centre-line model extracted from preoperative computed tomography (CT) is registered to the ultrasound data during surgery.
Results: Using the locally rigid ICP method, the RMS residual error when registering to a phantom was 0.7 mm, and the mean target registration error (TRE) for two in vivo porcine studies was 3.58 and 2.99 mm, respectively. Using the locally rigid landmark-based registration method gave a mean TRE of 4.23 mm using vessel centre lines derived from CT scans taken with pneumoperitoneum and 6.57 mm without pneumoperitoneum.
Conclusion: In this paper we propose a practical image-guided surgery system based on locally rigid registration of a CT-derived model to vascular structures located with LUS. In a physical phantom and during porcine laparoscopic liver resection, we demonstrate accuracy of target location commensurate with surgical requirements. We conclude that locally rigid registration could be sufficient for practically useful image guidance in the near future
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