Robotics Image Collection
Robotics Image Collection - Related Videos
11:26
Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues
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Cited by 20 •
2010
We report a method for introduction, tracking and quantitative analysis of GFP expression in plant cells. This method utilizes a custom-designed robotics system for semi-continuous image collection from large numbers of samples, over time. We also demonstrate the use of ImageJ and ImageReady for analysis of image series.
03:54
Robot-Assisted Radical Prostatectomy in a Heart Transplant Patient
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2026
Robot-assisted radical prostatectomy in a heart transplant recipient on tacrolimus was complicated by a tacrolimus-associated left ureteral leak and secondary anastomotic insufficiency, successfully managed with staged urinary diversion and multidisciplinary care.
11:01
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
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Cited by 12 •
2015
The overall goal of this method is to establish an SSVEP-based experimental procedure by integrating multiple software programs to enable the study of brain-robot interaction with humanoid robots, which is prospective in assisting the sick and elderly as well as performing unsanitary or dangerous jobs.
08:16
Adaptation of a Haptic Robot in a 3T fMRI
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Cited by 2 •
2011
The adaptation and use of a haptic robot in a 3T fMRI is described.
08:34
Robot-assisted Partial Splenectomy
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2026
This protocol describes a robot-assisted partial splenectomy for a benign splenic tumor. The robotic approach optimizes spleen preservation by enhancing surgical precision, minimizing blood loss, and improving postoperative outcomes. Key surgical steps include meticulous splenic hilum dissection, selective vascular ligation, intraoperative ultrasonography, and controlled parenchymal transection.
FAQs
Lighting, camera-to-specimen distance, orientation, and timing are central controls because changes in these conditions can alter how biological features appear from one image to the next. Keeping them defined makes images more comparable across specimens, locations, and experimental stages. This consistency supports quantitative analysis, phenotyping, classification, and machine-learning workflows built from the collected data.
Metadata connects each image to its biological and experimental context. Records such as location, orientation, and experimental stage help researchers determine where and when an image was acquired and how the specimen was positioned. That context strengthens interpretation, enables comparisons across observations, and makes image collections more useful for reproducible analysis rather than treating images as isolated files.
Systematic acquisition produces image datasets with more consistent visual conditions and linked contextual information. These characteristics can help automated methods learn patterns associated with organisms, tissues, behavior, or laboratory samples, while reducing variation caused by inconsistent manual collection. The resulting data can support automated classification, phenotyping, monitoring, and quantitative measurement when those analyses match the research objective.
A workflow begins by positioning or navigating the robotic platform relative to the specimen, followed by image capture under defined lighting, distance, and timing conditions. Each image is then linked with relevant metadata, including location, orientation, or experimental stage. The organized collection can subsequently be used for visual analysis, machine-learning workflows, monitoring, or quantitative measurement.
The approach is useful when manual sampling is slow, difficult, or inconsistent, and when researchers need observations distributed across time or space. It can document organisms, tissues, behavior, and laboratory samples, including situations in which access or repeated observation is challenging. Its value comes from expanding systematic observation while supporting more consistent downstream analysis.
Depending on the specimen and study design, the images can support automated classification, phenotyping, behavioral documentation, monitoring, and quantitative measurement. Because observations may be linked to location, orientation, and experimental stage, researchers can relate visual patterns to biological context. The datasets therefore serve both descriptive purposes, such as documenting organisms, and analytical purposes, such as comparing measured features.