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deep-learning–based tool  (Siemens Healthineers)

 
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    Structured Review

    Siemens Healthineers deep-learning–based tool
    Deep Learning–Based Tool, supplied by Siemens Healthineers, 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/deep-learning%E2%80%93based+tool/deep+learning+based+tool/pmc11647470-88-17-25
    Average 90 stars, based on 1 article reviews
    deep-learning–based tool - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    other:

    Article Title: Image navigator–based, automated coronary magnetic resonance angiography for the detection of coronary artery stenosis
    Article Snippet: The image slab including oversampling (yellow) and 2D image navigator (iNav) (blue) are automatically determined by a deep-learning–based tool which is part of work-in-progress software (Siemens Healthineers, cardiac dot companion). iNav-AUTO CMRA image navigator–based, automated coronary magnetic resonance angiography, 2D two-dimensional Image showing the graphical representation of cardiac motion provided by the user interface and the deep-learning–based calculation of the end-systolic rest period (A) and mid-diastolic rest period (B), both indicated by the orange regions, which can be used to determine the optimum acquisition window (C) for iNav-AUTO CMRA. iNav-AUTO CMRA image navigator–based, automated coronary magnetic resonance angiography.



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    Image Search Results


    Workflow of the pipeline for image processing and segmentation in the DeepShoot tool. Green and orange color boxes represent the operations of image segmentation and trait calculation: (A) original image, (B) original image patches of size 256 x 256, (C) segmented image patches of size 256 x 256, (D) binary segmentation of the original image, (E) RGB color space of (D) .

    Journal: Frontiers in Plant Science

    Article Title: Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot)

    doi: 10.3389/fpls.2022.906410

    Figure Lengend Snippet: Workflow of the pipeline for image processing and segmentation in the DeepShoot tool. Green and orange color boxes represent the operations of image segmentation and trait calculation: (A) original image, (B) original image patches of size 256 x 256, (C) segmented image patches of size 256 x 256, (D) binary segmentation of the original image, (E) RGB color space of (D) .

    Article Snippet: The deep learning-based shoot image analysis tool (DeepShoot) is designed for automated segmentation and quantification of visible light (VIS) images of arabidopsis, maize, and barley shoots acquired from greenhouse phenotyping experiments using LemnaTec-Scanalyzer3D high throughput plant phenotyping platforms (LemnaTec GmbH, Aachen, Germany). shows examples of arabidopsis, maize, and barely images from three different LemnaTec phenotyping platforms tailored to the screening of large, mid-size, and small plants.

    Techniques:

    Segmentation performance: first, second and third row represents the original RGB image, ground truth segmentation by the kmSeg tool and predicted segmentation by the DeepShoot tool, respectively. The DC of each image as following: (A) Arabidopsis side view: 0.9117, (B) Arabidopsis top view: 0.9876, (C) Barley side view: 0.9384, (D) Barley top view: 0.9617, (E) Maize side view: 0.9709, (F) Maize top view: 0.9843.

    Journal: Frontiers in Plant Science

    Article Title: Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot)

    doi: 10.3389/fpls.2022.906410

    Figure Lengend Snippet: Segmentation performance: first, second and third row represents the original RGB image, ground truth segmentation by the kmSeg tool and predicted segmentation by the DeepShoot tool, respectively. The DC of each image as following: (A) Arabidopsis side view: 0.9117, (B) Arabidopsis top view: 0.9876, (C) Barley side view: 0.9384, (D) Barley top view: 0.9617, (E) Maize side view: 0.9709, (F) Maize top view: 0.9843.

    Article Snippet: The deep learning-based shoot image analysis tool (DeepShoot) is designed for automated segmentation and quantification of visible light (VIS) images of arabidopsis, maize, and barley shoots acquired from greenhouse phenotyping experiments using LemnaTec-Scanalyzer3D high throughput plant phenotyping platforms (LemnaTec GmbH, Aachen, Germany). shows examples of arabidopsis, maize, and barely images from three different LemnaTec phenotyping platforms tailored to the screening of large, mid-size, and small plants.

    Techniques:

    Evaluation of neural network segmentation with respect to the proposed U-net on arabidopsis, barley and maize side view images respectively. (A) NN DC: 0.7824, DeepShoot DC: 0.8342, (B) NN DC: 0.6973, DeepShoot DC: 0.8924, (C) NN DC: 0.8746, DeepShoot DC: 0.9360.

    Journal: Frontiers in Plant Science

    Article Title: Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot)

    doi: 10.3389/fpls.2022.906410

    Figure Lengend Snippet: Evaluation of neural network segmentation with respect to the proposed U-net on arabidopsis, barley and maize side view images respectively. (A) NN DC: 0.7824, DeepShoot DC: 0.8342, (B) NN DC: 0.6973, DeepShoot DC: 0.8924, (C) NN DC: 0.8746, DeepShoot DC: 0.9360.

    Article Snippet: The deep learning-based shoot image analysis tool (DeepShoot) is designed for automated segmentation and quantification of visible light (VIS) images of arabidopsis, maize, and barley shoots acquired from greenhouse phenotyping experiments using LemnaTec-Scanalyzer3D high throughput plant phenotyping platforms (LemnaTec GmbH, Aachen, Germany). shows examples of arabidopsis, maize, and barely images from three different LemnaTec phenotyping platforms tailored to the screening of large, mid-size, and small plants.

    Techniques:

    Graphical User Interface of the DeepShoot tool: left, middle, and right images represent the original image, predicted probability map, and predicted color image, respectively.

    Journal: Frontiers in Plant Science

    Article Title: Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot)

    doi: 10.3389/fpls.2022.906410

    Figure Lengend Snippet: Graphical User Interface of the DeepShoot tool: left, middle, and right images represent the original image, predicted probability map, and predicted color image, respectively.

    Article Snippet: The deep learning-based shoot image analysis tool (DeepShoot) is designed for automated segmentation and quantification of visible light (VIS) images of arabidopsis, maize, and barley shoots acquired from greenhouse phenotyping experiments using LemnaTec-Scanalyzer3D high throughput plant phenotyping platforms (LemnaTec GmbH, Aachen, Germany). shows examples of arabidopsis, maize, and barely images from three different LemnaTec phenotyping platforms tailored to the screening of large, mid-size, and small plants.

    Techniques:

    Frequency of cryptic splice-altering MYBPC3 variants in a large cohort of HCM probands.

    Journal: Scientific Reports

    Article Title: Identification of an elusive spliceogenic MYBPC3 variant in an otherwise genotype-negative hypertrophic cardiomyopathy pedigree

    doi: 10.1038/s41598-022-11159-y

    Figure Lengend Snippet: Frequency of cryptic splice-altering MYBPC3 variants in a large cohort of HCM probands.

    Article Snippet: The probability of a variant being splice-altering was estimated in silico using the deep learning-based tool SpliceAI with a window of ± 200 bp (maximum distance of analyzed nucleotides from the variant) unless otherwise indicated, and the splicing prediction module of the software package Alamut Visual (v.2.11.0, Interactive Biosoftware).

    Techniques: