Marker-Free Detection of Instruments in Laparoscopic Images to Control a Cameraman Robot

Assistant robots are widely used in laparoscopic surgery to facilitate the camera holding and manipulation task. A variety of a hands-free operator interfaces have been implemented for user control of the robots, including voice commands, foot pedals, and eye and head motion tracking systems. This paper proposes a novel user control interface, based on processing of the laparoscopic images, that enables the robot to automatically adjust the view of the laparoscopic camera without disturbing the surgeon’s concentration. An effective marker-free detection method was investigated to track the instrument position in the laparoscopic images in real time so that the robot could center the instrument tip in the camera view. Considering several available methods it was found that a color space analysis, based on the quantitative comparison of the background’s and instrument’s pixels color contexts, provides the best results. The color contexts were presented in covariance matrix and mean values and analyzed using Mahalanobis distance measure in RGB color space. Tests on laparoscopic images with controlled conditions, e.g., sufficient light and low noises, revealed 86 percent correct detection with a processing rate of 3.7 frames per second on a conventional PC. Further work is going on to improve the algorithm.

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SU-G-BRA-10: Marker Free Lung Tumor Motion Tracking by An Active Contour Model On Cone Beam CT Projections for Stereotactic Body Radiation Therapy of Lung Cancer

Medical Physics ◽

10.1118/1.4956934 ◽

2016 ◽

Vol 43 (6Part25) ◽

pp. 3637-3637

Author(s):

M Chao ◽

J Wei ◽

Y Yuan ◽

Y Lo

Keyword(s):

Lung Cancer ◽

Active Contour ◽

Motion Tracking ◽

Stereotactic Body Radiation Therapy ◽

Active Contour Model ◽

Lung Tumor ◽

Cone Beam Ct ◽

Cone Beam ◽

Contour Model ◽

Marker Free

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A GPU-Based Automatic Approach for Guide Wire Tracking in Fluoroscopic Sequences

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001419540259 ◽

2019 ◽

Vol 33 (08) ◽

pp. 1954025

Author(s):

Ken Chen ◽

Cheng Wang ◽

Yaoqin Xie ◽

Shoujun Zhou

Keyword(s):

Real Time ◽

Detection Rate ◽

Motion Tracking ◽

Distance Measure ◽

Guide Wire ◽

Data Sets ◽

Training Time ◽

Tracking Method ◽

Main Challenge ◽

Improve Calculation

Guide wire tracking in fluoroscopic images has done a significant task in assisting the physicians during radiology-aided interventions. Many groups have tried to detect the guide wire from the fluoroscopic images based on the image properties. The main challenge is that manual intervention is required during the detection. Other groups try to introduce localizers to track guide wires during intervention, which requires additional hardware equipment, and may intervene with the traditional clinical routines. Machine learning methods are also exploited. Although such methods may provide accurate tracking, they often require large amount of data and training time. In this paper, we propose a GPU-based fast and automatic approach to track guide wires in fluoroscopic sequences. We propose a multi-scale filtering and gradient vector field-based real-time tracking method for guide wire tracking from fluoroscopic images. To improve calculation efficiency and meet real-time application requirement, we propose a GPU-based acceleration scheme, and also a Bayesian filter-like motion tracking method to limit the guide wire tracking to a smaller range to improve calculation efficiency. We test our proposed method on two test data sets of fluoroscopic sequences of 102 frames and 72 frames. We achieve an average guide wire detection rate of 96.7%, a false detection rate of 0.0011% and an error distance measure of 0.83 pixels for the first sequence, and 98.8%, 0.000069% and 0.85 pixels, respectively, for the second sequence. With the proposed acceleration method, we finish calculation for the first sequence in nine seconds, thus, efficiency is enhanced by 100 times with the unaccelerated algorithm.

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Beyond Percent Correct: Measuring Change in Individual Picture Naming Ability

Journal of Speech Language and Hearing Research ◽

10.1044/2021_jslhr-20-00205 ◽

2021 ◽

pp. 1-23

Author(s):

Grant M. Walker ◽

Alexandra Basilakos ◽

Julius Fridriksson ◽

Gregory Hickok

Keyword(s):

Picture Naming ◽

Distance Measure ◽

Item Difficulty ◽

Control Group ◽

Specific Response ◽

Percent Correct ◽

Treatment Groups ◽

Accuracy Measure ◽

Different Response ◽

Changes Over Time

Purpose: Meaningful changes in picture naming responses may be obscured when measuring accuracy instead of quality. A statistic that incorporates information about the severity and nature of impairments may be more sensitive to the effects of treatment. Method: We analyzed data from repeated administrations of a naming test to 72 participants with stroke aphasia in a clinical trial for anomia therapy. Participants were divided into two groups for analysis to demonstrate replicability. We assessed reliability among response type scores from five raters. We then derived four summary statistics of naming ability and their changes over time for each participant: (a) the standard accuracy measure, (b) an accuracy measure adjusted for item difficulty, (c) an accuracy measure adjusted for item difficulty for specific response types, and (d) a distance measure adjusted for item difficulty for specific response types. While accuracy measures address the likelihood of a correct response, the distance measure reflects that different response types range in their similarity to the target. Model fit was assessed. The frequency of significant improvements and the average magnitude of improvements for each summary statistic were compared between treatment groups and a control group. Effect sizes for each model-based statistic were compared with the effect size for the standard accuracy measure. Results: Interrater and intrarater reliability were near perfect, on average, though compromised somewhat by phonological-level errors. The effects of treatment were more evident, in terms of both frequency and magnitude, when using the distance measure versus the other accuracy statistics. Conclusions: Consideration of item difficulty and response types revealed additional effects of treatment on naming scores beyond those observed for the standard accuracy measure. The results support theories that assume naming ability is decomposable into subabilities rather than being monolithic, suggesting new opportunities for measuring treatment outcomes. Supplemental Material https://doi.org/10.23641/asha.17019515

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