Learn more about publications and scientific research involving Quantib.

 

Quantib® ND

Comparing two artificial intelligence software packages for normative
brain volumetry in memory clinic imaging

Zaki, Vernooij, Smits, Tolman, Papma, Visser, Steketee.

Neuroradiology, 2022
  • This study compares diagnostic reports from two AI software packages and show high inter-rater and high accuracy and sensitivity agreement when distinguishing normal and abnormal profiles.
  • Radiological assessment of the MRI results, clinical profile and an understanding of the software used are needed for correct diagnostic interpretation, as results from software packages are not interchangeable.

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How Machine learning is embedded to support clinician decision making: an analysis of FDA-approved medical devices

Lyell, Coiera, Chen, Shah, Magrabi.

BMJ Health & Care Informatics, 2021

Thumbnails - scientific resources - Lyell et al.

  • This study reviews the use in clinical practice, the benefits, the consequences and the possible risks of FDA cleared devices that use machine learning (up to early 2020). 
  • In this study, Quantib® ND and Quantib® Brain were are reviewed as assistive machine learning devices for quantification.

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Distinctive pattern of temporal atrophy in patients with frontotemporal dementia and the I383V variant in TARDBP

Mol et al.,

Journal of Neurology, Neurosurgery & Psychiatry, 2021

Thumbnails - scientific resources - Mol et al.

  • This study examines the genetic cause of frontotemporal dementia and finds a distinctive pattern of temporal atrophy in patients with one specific gene-variant.
  • In this study, Quantib® ND was used for quantitative assessment of volume loss across lobar brain regions.

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AI in de Neuroradiologische praktijk. Een aanzienlijke verandering en verbetering

Prof. Smits.

Memorad, Nedelandse Vereniging voor Radiologie, 2021

Thumbnails - scientific resources - Marion Smits

  • Commentary on Quantib® ND, and its usefulness in clinical practice (in Dutch).

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Overview of MR Imaging Volumetric Quantification in Neurocognitive Disorders

Raji, Ly, Benzinger.

Topics in Magnetic Resonance Imaging, 2019

Thumbnails - scientific resources - Raji et al.

  • This study reviews the history and methods for volumetric quantification, as well as acquisition protocols for multiple cognitive disorders. 
  • In this study, Quantib® ND is listed as one of the FDA cleared software solutions.

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Aortic stiffness and brain integrity in elderly patients with cognitive and functional complaints

Tap, van Opbroek, Niessen, Smits, Mattace-Raso.

Clinical Interventions in Aging, 2018

Thumbnails - scientific resources - Tap et al.

  • This study shows a partial association between higher aortic stiffness and increased volume of WMH and decreased volume of grey matter in older patients with cognitive and functional complaints.
  • In this study, Quantib® ND was used to segment brain tissue, and WMH.

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Gray matter heritability in family-based and population-based studied using voxel-based morphometryortic 

Van der Lee et al.,

Human Brain Mapping, 2017

Thumbnails - scientific resources - Van der Lee et al.

  • This study looks at the influence of the genetics on gray matter heritability and showed that construction of reliable heritability maps of gray matter voxels is feasible.
  • In this study, Quantib methods were used for brain tissue segmentation.

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Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning 

Fortunati et al.,

Physics in Medicine & Biology, 2015
  • Fortunati et al. investigate atlas based segmentation combined with a local intensity based classification algorithm for segmentation of different brain structures.
  • Quantib™ ND uses the same method for its hippocampus segmentation.

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Accuracy and reproducibility study of automatic MRI brain tissue segmentation methods 

de Boer et al.,

NeuroImage, 2010
  • De Boer et al. investigate several methods for follow-up analysis of brain structure segmentations.
  • Quantib™ ND brain segmentation algorithm uses the automatically trained kNN classifier.

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White matter lesion extension to automatic brain tissue segmentation on MRI

de Boer et al.,

NeuroImage, 2009
  • De Boer et al. describe the underlying methods used for the Quantib™ ND algorithm development of the Quantib™ ND brain structure segmentation and white matter hyperintensity segmentation as included in Quantib™ ND.
  • For further reading we refer you to the PhD thesis of Renske de Boer, the product owner of Quantib ND.

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Quantib® Prostate

Validation of Quantib software in the mp-MRI detection of Prostate Cancer lesions  

Faiella et al.,

ECR overture poster presentation, ECR 2022

Scientific resources - Faiella et al

  • Authors compared the performance of an inexperienced radiologist (supported by Quantib Prostate 1.3 ) to the performance of an expert radiologist with more than 8 years of experience
  • The radiologist supported by Quantib Prostate, performed with a higher sensitivity and PPV regardless of lesion location and with a higher sensitivity for all lesions found regardless of their determined Gleason score after biopsy. Additionally, the use of Quantib Prostate increased the PPV for lesions given PI-RADS scores of 3 and 5.

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An AI-powered radiomics model for the detection of extraprostatic extension

Van den berg et al.,

ISUI conference presentation 2021

Scientific resources - van den Berg et al

  • Won best abstract award
  • This work assessed new radiomic features for the prediction of extraprostatic extension (EPE) on lesion level using artificial intelligence.
  • The AI-powered radiomics model achieved good diagnostic discrimination for lesion-specific EPE prediction, with lesion volume and tumor contact volume being most predictive.

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Effect of varying DWI b values on prostate lesion segmentation accuracy and robustness

Postigo Filiquete et al.,

ESMRMB conference presentation 2021

Scientific resources - Postigo Filiquete et al

  • They investigated prostate lesion segmentation accuracy by training a segmentation network using acquired DWI (aDWI) or computed DWI (cDWI) at different b values. 
  • The results showed, best segmentation accuracy with a network trained and tested with high b values (b1500), robustness against different b values during training, and a general finding that lesion segmentation networks are less sensitive to different measured b values, than to computed higher b values.

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Quantib Brain

Accuracy of the compressed sensing accelerated 3D-Flair sequence for the detection of MS plaques at 3T

Toledano-Massiah et al., 

American Journal of Neuro-Radiology, 2018

Thumbnails - scientific resources - Toledano-Massiah et al.

  • The authors evaluate the diagnostic performance for the detection of MS lesion of 3D-Flair sequences without and with compressed sensing. Diagnostic performance is preserved with faster acquisition in compressed sensing of 3D-Flair sequences.
  • In this study, Quantib Brain was used to quantify the volume and total number of MS lesions on both Flair sequences.

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Quantib AI algorithm for Body Composition

Evaluation of a fully automatic deep learning-based method for the measurement of psoas muscle area

Van Erck et al.,

FRONTIERS IN NURITION, 2022
  • The study evaluates if fully automatic DL methods can substitute manual psoas annotation, for the assessment of muscle mass. Manual segmentation is cumbersome and time consuming but has shown to be a good marker for malnutrition, sarcopenia, and adverse outcomes.
  • Assessment with Quantib Body Composition software demonstrates accurate performance and is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications. 

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Towards personalized contrast injection: artificial-intelligence-derived body composition and liver enhancement in computed tomography

De Jong, Veldhuis, Wessels, de Vos, Moeskops, Kok.

Journal of personalized medicine, 2021

Thumbnails - scientific resources - De Jong et al.

  • This study shows that contrast injections should be personalized. Liver enhancement is more strongly associated with the percentage of lean body weight than with total body weight or body mass index.
  • In this study, Quantib’s AI algorithm for body composition was used to segment and determine the body composition and calculated percentages of lean body weight. 

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Automatic quantification of body composition at L3 vertebra level with convolutional neural networks

Moeskops, de Vos, Veldhuis, de Jong, Išgum, Leiner.

ECR 2020 conference poster

Thumbnails - scientific resources - Moeskops et al.

  • Investigation of feasibility of a deep learning-based method for automatic segmentation of fat, visceral fat, psoas muscle, abdominal muscle and long spine muscles at the level of L3

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Automatic Quantification of 3D Body Composition from Abdominal CT with an Ensemble of Convolutional Neural Networks

Moeskops et al.,

RSNA 2019 conference pRESENTATION
  • This study shows that accurate fully automatic segmentation of subcutaneous fat, visceral fat and psoas muscle from full abdominal CT scans is feasible.
  • The proposed method allows fast and fully automatic analysis of 3D body composition in abdominal CT that can aid in individualized risk assessment in cardiovascular disease and cancer.

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