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sliceomatic  (TomoVision Inc)

 
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    Structured Review

    TomoVision Inc sliceomatic
    Sliceomatic, supplied by TomoVision Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/otsu+thresholding+algorithm/sliceomatic/pm38300360-63-24-25
    Average 90 stars, based on 1 article reviews
    sliceomatic - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    other:

    Article Title: Body Composition Assessment Provides Prognostic Information in Patients With Cancer Affected by Chronic Graft vs. Host Disease.
    Article Snippet: We quantified tissue cross- sectional areas with SliceOmatic® (TomoVision, Magog, Quebec, Canada).

    Software:

    Article Title: Visceral and subcutaneous adipose tissue in children born after ART with frozen and fresh embryo transfers
    Article Snippet: .. The volumes of VAT, SAT, and non-adipose tissue (NAT) were semi-automatically quantified from the scans with commercially available software (SliceOmatic, version 5.0 Rev-10b, Tomovision Inc., Montreal, Canada) based on signal intensity of adipose tissue. ..

    Article Title: Quantifying Leg Muscle Disuse Atrophy During Bed Rest Using DXA, CT, and MRI.
    Article Snippet: .. Examples include, but are not limited to, SliceOmatic (Tomovision, Quebec, Canada), Springbok Analytics (Charlottesville, VA, USA), Dafne (Deep Anatomical Federation Network; open‐source software platform), 3D slicer (open‐source software platform), Materialise Mimics (Materialise NV, Leuven, Belgium), and AMRA researcher (AMRA Medical AB, Linköping, Sweden). ..

    Article Title: Stand-alone MRI tool for semiautomatic volumetry of abdominal adipose compartments in patients with obesity.
    Article Snippet: .. Over the years, a variety of software tools have been deliberately used for the segmentation of adipose tissue, among them sliceOmatic (Tomovision, Magog, QC, Canada), Osirix11, CoreSlicer12, ITK-SNAP13, MIPAV14, 3D Slicer15 or ImageJ16. ..

    Article Title: Skeletal Muscle Mass Loss Leads to Prolonged Mechanical Ventilation and Higher Tracheotomy Rates in Critically Ill Patients
    Article Snippet: .. To determine the CSA, we used the National Institutes of Health (NIH) ImageJ software (Version 1.53t), which represents a qualitatively equivalent alternative for body composition analyses as techniques using SliceOmatic (TomoVision, Montréal, QC, Canada) [ , ]. ..

    Article Title: Stand-alone MRI tool for semiautomatic volumetry of abdominal adipose compartments in patients with obesity
    Article Snippet: .. Over the years, a variety of software tools have been deliberately used for the segmentation of adipose tissue, among them sliceOmatic (Tomovision, Magog, QC, Canada), Osirix , CoreSlicer , ITK-SNAP , MIPAV , 3D Slicer or ImageJ . ..

    Article Title: Skeletal Muscle Mass Loss Leads to Prolonged Mechanical Ventilation and Higher Tracheotomy Rates in Critically Ill Patients.
    Article Snippet: .. To determine the CSA, we used the National Institutes of Health (NIH) ImageJ software (Version 1.53t), which represents a qualitatively equivalent alternative for body composition analyses as techniques using SliceOmatic (TomoVision, Montréal, QC, Canada) [12,13]. ..

    Article Title: Patellar cartilage thickness relates to knee external rotation during squatting in individuals with and without patellofemoral pain—a pilot study
    Article Snippet: .. The medial and lateral patellar cartilage was manually segmented by a trained investigator using commercial software (sliceOmatic, Tomovision, Montreal, Québec, Canada) ( ). .. Cartilage thickness was calculated by measuring the perpendicular distance between opposing voxels that defined the boundaries of the patellar cartilage, using a custom MATLAB program (MathWorks, Natick, Massachusetts).



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    Image Search Results


    Artificial Neural Network (ANN) applications in hyperspectral image analysis of food products.

    Journal: Current Research in Food Science

    Article Title: Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

    doi: 10.1016/j.crfs.2021.01.002

    Figure Lengend Snippet: Artificial Neural Network (ANN) applications in hyperspectral image analysis of food products.

    Article Snippet: Detection of stored insects in rice and maize , 400–1000 , Normalization , Otsu algorithm for image thresholding , Back Propagation Neural Network , 01 , – , 03 , – , 01 , – , 60:40 , MATLAB R2009b , 98% , .

    Techniques: Biomarker Discovery, Software

    Deep Learning (DL) applications in hyperspectral image analysis of food products.

    Journal: Current Research in Food Science

    Article Title: Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

    doi: 10.1016/j.crfs.2021.01.002

    Figure Lengend Snippet: Deep Learning (DL) applications in hyperspectral image analysis of food products.

    Article Snippet: Detection of stored insects in rice and maize , 400–1000 , Normalization , Otsu algorithm for image thresholding , Back Propagation Neural Network , 01 , – , 03 , – , 01 , – , 60:40 , MATLAB R2009b , 98% , .

    Techniques: Biomarker Discovery, Software, Activation Assay, Control, Extraction

    Support Vector Machines (SVM) applications in hyperspectral image analysis of food products.

    Journal: Current Research in Food Science

    Article Title: Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

    doi: 10.1016/j.crfs.2021.01.002

    Figure Lengend Snippet: Support Vector Machines (SVM) applications in hyperspectral image analysis of food products.

    Article Snippet: Detection of stored insects in rice and maize , 400–1000 , Normalization , Otsu algorithm for image thresholding , Back Propagation Neural Network , 01 , – , 03 , – , 01 , – , 60:40 , MATLAB R2009b , 98% , .

    Techniques: Plasmid Preparation, Biomarker Discovery, Software, Infection, Sampling

    Decision trees (DT) applications in hyperspectral image analysis of food products.

    Journal: Current Research in Food Science

    Article Title: Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

    doi: 10.1016/j.crfs.2021.01.002

    Figure Lengend Snippet: Decision trees (DT) applications in hyperspectral image analysis of food products.

    Article Snippet: Detection of stored insects in rice and maize , 400–1000 , Normalization , Otsu algorithm for image thresholding , Back Propagation Neural Network , 01 , – , 03 , – , 01 , – , 60:40 , MATLAB R2009b , 98% , .

    Techniques: Biomarker Discovery, Software, Selection, Infection

    Random Forest (RF) applications in hyperspectral image analysis of food products.

    Journal: Current Research in Food Science

    Article Title: Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

    doi: 10.1016/j.crfs.2021.01.002

    Figure Lengend Snippet: Random Forest (RF) applications in hyperspectral image analysis of food products.

    Article Snippet: Detection of stored insects in rice and maize , 400–1000 , Normalization , Otsu algorithm for image thresholding , Back Propagation Neural Network , 01 , – , 03 , – , 01 , – , 60:40 , MATLAB R2009b , 98% , .

    Techniques: Biomarker Discovery, Software, Virus, Infection

    k-Nearest Neighbor (k-NN) applications in hyperspectral image analysis of food products.

    Journal: Current Research in Food Science

    Article Title: Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

    doi: 10.1016/j.crfs.2021.01.002

    Figure Lengend Snippet: k-Nearest Neighbor (k-NN) applications in hyperspectral image analysis of food products.

    Article Snippet: Detection of stored insects in rice and maize , 400–1000 , Normalization , Otsu algorithm for image thresholding , Back Propagation Neural Network , 01 , – , 03 , – , 01 , – , 60:40 , MATLAB R2009b , 98% , .

    Techniques: Biomarker Discovery, Software, Residue, Extraction