Color-coded visualization of magnetic resonance imaging multiparametric maps

Kather, Jakob Nikolas; Weidner, Anja; Attenberger, Ulrike; Bukschat, Yannick; Weis, Cleo–Aron; Weis, Meike; Schad, Lothar R.; Zöllner, Frank G.

doi:10.1038/srep41107

Cited by 18 publications

(12 citation statements)

References 36 publications

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“…(Colour-coded) cancer probability heat maps and the use of transparency to reflect low probabilities or low classification confidence seem to be the most advanced automatic visualisation methods. Beyond PCa research, techniques are being developed to present multiple maps by colour [234] , targets, or transparency to the operator. The effectiveness of these methods remains to be investigated.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Wildeboer

Sloun

Wijkstra

et al. 2020

Computer Methods and Programs in Biomedicine

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DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Wildeboer

Sloun

Wijkstra

et al. 2020

Computer Methods and Programs in Biomedicine

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“…The latter category included pictures of landscapes, plants, trees, and object photographs in a gray scale background. Grayscale images provide very little information and elicit less interest compared to coloured images, hence it can reduced the involvement of the human visual system in extracting information from the images (Kather et al 2017).…”

Section: Methodsmentioning

confidence: 99%

Validation of Emotional Stimuli Flashcards for Conducting ‘Response to Reward’ fMRI study among Malaysian students

Nasser

Sharifat

Rashid

et al. 2020

Preprint

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Problematic Instagram Use (PIGU) is a specific−Internet−addiction disorder observed among the youth of today. fMRI, is able to objectively assess regional brain activation in response to addiction−specific rewards, e.g. viewing picture flashcards. Pictures uploaded onto Instagram by PIGUs have often been associated with risky behaviours in their efforts to gain more ′Likes′, thus it is hypothesized that PIGUs are more drawn to ′Negative−Emotional′ cues. To date, there is no local database with addiction−specific cues. Objective: To conduct an out−of−scanner validation study to create a database of pictures using ′Negative−Emotional′ cues that evoke responses of arousal among PIGUs. Method: Forty−four Malaysian undergraduate students (20 PIGUs, 24 controls) were randomly recruited based on the evaluation using the Smartphone−Addiction−Scale−Malay version (SAS−M) and modified Instagram Addiction Test (IGAT); and fulfilled Lin et al. (2016) definition of addiction. They were administered 200 content-specific pictures that were multidimensional i.e. arousal (excitation/relaxation effects), approach−avoidance (motivational direction) and emotional valence (positive/negative feelings) to describe the PIGUs perception of the psychological properties of the pictures using a 9−point Likert scale. Results: PIGUs viewing ′Negative−Emotional′ cues demonstrated significant positive correlations between arousal & valence (z = 4.834, p < .001, effect size = 0.69) and arousal & avoidance−approach (z = 4.625, p < .001, effect size= 0.66) as compared to controls and were more frequently aroused by ′Negative−Emotional′ type of stimuli. Conclusion: A database of validated, addiction−specific pictures can be developed to potentiate any future cue−induced response to reward fMRI studies to assess PIGU. Keywords: Addiction, Affective ratings, Cravings, Picture database, Reward

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“…The CIELab is a color space defined by the International Commission on Illumination (CIE) in 1976 (37,38). The unique goal of the L*a*b* model is to be 'deviceindependent'.…”

Section: Cielab Computationmentioning

confidence: 99%

“…Target signatures were inserted into supervised target detection algorithms (STDA) (35) to non-invasively detect PCa at the voxel level and to measure the tumor volume. The tumor appearance was enhanced through colors (yellow) (36,37) visually identified by a reader and by also applying a version of the CIELAB process (38) to the multi-spectral hypercubes. The techniques described in the manuscript are general and apply for any MRI scanner that collect the requisite images.…”

Section: Introductionmentioning

confidence: 99%

Algorithms applied to spatially registered multi-parametric MRI for prostate tumor volume measurement

Mayer¹,

Simone²,

Türkbey

et al. 2021

Quant Imaging Med Surg

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Background: Prostate tumor volume correlates with critical components of cancer staging such as Gleason score (GS) grade, predicted disease progression, and metastasis. Therefore, non-invasive tumor volume measurement may elevate clinical management. Radiology assessments of multi-parametric MRI (MP-MRI) commonly visually examine individual images to determine possible tumor presence. This study combines registered MP-MRI into a single image that display normal tissue and possible lesions. This study tests and exploits the vector nature of spatially registered MP-MRI by using supervised target detection algorithms (STDA) and color display and psychovisual analysis (CIELAB) to non-invasively estimate prostate tumor volume.Methods: MRI, including T1, T2, diffusion [apparent diffusion coefficient (ADC)], dynamic contrast enhanced (DCE) images, were resampled, rescaled, translated, and stitched to form spatially registered Multi-parametric cubes. The multi-parametric or multi-spectral signatures (7-component or T1, T2, ADC, etc.) that characterize the prostate tumors were inserted into target detection algorithms with conical decision surfaces (adaptive cosine estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. In addition, tumor appeared as yellow in color images that were created by assigning red to washout from DCE, green to high B from diffusion, and blue to autonomous diffusion image. The yellow voxels in the three-channel hypercube were visually identified by a reader and recording voxels that exceed a threshold in the b* component of the CIELAB algorithm. The number of reported tumor voxels were converted to volume based on spatial resolution and slice separation. The tumor volume measurements were quantitatively validated by comparing the tumor volume computations to the pathologist's assessment of the histology of sectioned whole mount prostates from 26 consecutive patients with prostate adenocarcinoma who underwent radical prostatectomy. This study analyzed tumors exceeding 1 cc and that also took up contrast material (18 patients).Results: High correlation coefficients for tumor volume measurements using supervised target detection and color analysis vs. histology from wholemount prostatectomy were computed (R=0.83 and 0.91, respectively). A linear fit for tumor volume measurements using for supervised target detection and color analysis vs. tumor measurements from radical prostatectomy (after correcting for shrinkage from the radical prostatectomy) results in a slope of 1.02 and 3.02, respectively. A polynomial fit for the color analysis to the histology found (R=0.95). Voxels exceeding a threshold in the b* part of the CIELAB algorithm yielded correlation coefficients (0.71, 0.80) offsets (0.01 cc, −0.63 cc) and slopes (1.99, 0.89) against the wholemount prostatectomy and color analysis, respectively.Conclusions: Supervised target detection and color display and analysis applied to registered MP-MRI non-invasively estimates prostate tumor volumes >1 cc and...

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Color-coded visualization of magnetic resonance imaging multiparametric maps

Cited by 18 publications

References 36 publications

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Validation of Emotional Stimuli Flashcards for Conducting ‘Response to Reward’ fMRI study among Malaysian students

Algorithms applied to spatially registered multi-parametric MRI for prostate tumor volume measurement

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