Medical Imaging Software
Medical Imaging Software - Related Videos
08:32
Imaging C. elegans Embryos using an Epifluorescent Microscope and Open Source Software
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Cited by 8 •
2011
The C. elegans embryo is a powerful system for studying cell biology and development. We present a protocol for live imaging of C. elegans embryos utilizing DIC optics or fluorescence using readily available epifluorescent microscopes and open-source software.
08:21
Wideband Optical Detector of Ultrasound for Medical Imaging Applications
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Cited by 4 •
2014
Optical detection of ultrasound is impractical in many imaging scenarios because it often requires stable environmental conditions. We demonstrate an optical technique for ultrasound sensing in volatile environments with miniaturization and sensitivity levels appropriate for optoacoustic imaging in restrictive scenarios, e.g. intravascular applications.
08:39
High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately
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Cited by 117 •
2014
We present a high-throughput image analysis software application to measure the size of three-dimensional tumor spheroids imaged with bright-field microscopy. This application provides a fast and effective way to examine the effects of therapeutic drugs on spheroids, which is beneficial for researchers who wish to use spheroids in drug screens.
07:03
Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
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Cited by 6 •
2017
This article describes the design and development of a sterilizable custom camera optical distortion calibration target for the peri-operative, fluid-immersed calibration of endoscopes during endoscopic interventions.
09:36
Safety Precautions and Operating Procedures in an (A)BSL-4 Laboratory: 4. Medical Imaging Procedures
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Cited by 8 •
2016
Here, we present an overview of the preparation and animal handling procedures required to safely perform medical imaging in an animal biosafety level 4 laboratory. Computed tomography of a mock-infected guinea pig illustrates these procedures that may be used to evaluate the disease caused by a high consequence pathogen.
FAQs
Reconstruction converts data produced by an imaging modality into an image that can be interpreted. The resulting representation provides the foundation for later operations, including contrast enhancement, segmentation, visualization, and three-dimensional rendering. Because different modalities produce different forms of imaging data, reconstruction is an important step in making those data suitable for clinical assessment or biomedical analysis.
Contrast enhancement adjusts image presentation so differences within the represented anatomy become easier to inspect. It supports visualization by emphasizing features that may otherwise be difficult to distinguish in the image. This operation does not replace clinical interpretation, but it can make the software output more useful for detecting abnormalities, examining anatomy, and reviewing changes across studies.
Segmentation separates or identifies anatomical structures or regions within an image, creating a basis for measuring them. Once relevant regions are delineated, software can support quantitative analysis rather than relying only on visual inspection. In medicine, this can assist anatomical assessment, disease monitoring, and evaluation of treatment response when measurements are clinically or scientifically relevant.
Three-dimensional rendering presents image information as a spatial representation that can support examination of anatomy and procedure planning. Machine learning adds computational analysis that can help produce more consistent interpretations and quantitative assessments. These approaches address different needs: rendering emphasizes spatial visualization, whereas machine learning and related analysis support pattern interpretation and new personalized-care strategies.
A typical workflow begins with image acquisition from a modality such as magnetic resonance imaging, computed tomography, ultrasound, or digital radiography. The software then reconstructs and processes the data, applies visualization or analytical operations, and presents results for interpretation. Integration with image archives and electronic health records can connect these outputs with existing clinical information and improve workflow.
The software can support detection of abnormalities, measurement of anatomy, monitoring of disease progression, procedure planning, and assessment of treatment response. Its value depends on how image processing and analysis contribute to the specific clinical task. By providing interpretable visual and quantitative information, it can help clinicians examine findings and follow changes over time.
In biomedical research, image processing and quantitative analysis help investigators examine internal structures and evaluate changes represented in imaging data. Machine learning can support more consistent interpretation and enable new analytical approaches. When these capabilities are connected with clinical imaging and related records, they can contribute to personalized care by informing assessment and treatment evaluation.