A User Interface for Optimizing Radiologist Engagement in Image Data                     Curation for Artificial Intelligence

Demirer, Mutlu; Candemir, Sema; Bigelow, Matthew T.; Yu, Sisheng; Gupta, Vikash; Prevedello, Luciano M.; White, Richard D.; Yu, Joseph S.; Grimmer, Rainer; Wels, Michael; Wimmer, Andreas; Halabi, Abdul H.; Ihsani, Alvin; O’Donnell, Thomas; Erdal, Barbaros S.

doi:10.1148/ryai.2019180095

Cited by 26 publications

(20 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to display the nodules to dedicated thoracic radiologists (CCA and GFMI each with 6–8 years of post-fellowship experience), a custom Graphical User Interface (GUI) [ 12 ] was built [MeVisLab version 2.8, Windows 64 bit, VS2013: Bremen, Germany ( https://www.mevislab.de/ )] ( Fig 1 ). This GUI integrated the functionalities of a commercial nodule segmentation algorithm [syngo.via MM Oncology: Siemens Healthineers, Forchheim, Germany ( https://www.siemens-healthineers.com/medical-imaging-it/syngoviaspecialtopics/syngo-via-for-oncology )] as they would appear within an established commercial post-processing platform [syngo.via: Siemens Healthineers, Forchheim, Germany ( https://www.healthcare.siemens.com/medical-imaging-it/advanced-visualization-solutions/syngovia )].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

et al. 2020

Self Cite

View full text Add to dashboard Cite

Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of volume-, density-, and texture-based features (outcome variables) over routine ranges of radiation dose, reconstruction kernel, and slice thickness. CT raw data of 23 nodules were reconstructed using 320 acquisition/reconstruction conditions (combinations of 4 doses, 10 kernels, and 8 thicknesses). Scans at 12.5%, 25%, and 50% of protocol dose were simulated; reduced-dose and full-dose data were reconstructed using conventional filtered back-projection and iterative-reconstruction kernels at a range of thicknesses (0.6–5.0 mm). Full-dose/B50f kernel reconstructions underwent expert segmentation for reference Region-Of-Interest (ROI) and nodule volume per thickness; each ROI was applied to 40 corresponding images (combinations of 4 doses and 10 kernels). Typical texture analysis metrics (including 5 histogram features, 13 Gray Level Co-occurrence Matrix, 5 Run Length Matrix, 2 Neighboring Gray-Level Dependence Matrix, and 3 Neighborhood Gray-Tone Difference Matrix) were computed per ROI. Reconstruction conditions resulting in no significant change in volume, density, or texture metrics were identified as “compatible pairs” for a given outcome variable. Our results indicate that as thickness increases, volumetric reproducibility decreases, while reproducibility of histogram- and texture-based features across different acquisition and reconstruction parameters improves. To achieve concomitant reproducibility of volumetric and radiomic results across studies, balanced standardization of the imaging acquisition parameters is required.

show abstract

Section: Methodsmentioning

confidence: 99%

“…In order to display the nodules to dedicated thoracic radiologists (CCA and GFMI each with 6-8 years of post-fellowship experience), a custom Graphical User Interface (GUI) [12] was built [MeVisLab version 2.8, Windows 64 bit, VS2013: Bremen, Germany (https://www. mevislab.de/)] (Fig 1).…”

Section: Segmentation and Volume Measurementmentioning

confidence: 99%

Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ground-truth BM segmentation masks were prepared by a radiologist, using a custom-built tool for the project [34]. The tool was developed using MeVisLab 2.8 (medical image processing and visualization framework developed by MeVis Medical Solutions AG), and it allows users to load volumetric MRI datasets, manually delineate the borders of BM, and edit the existing segmentation masks if needed (see Fig.5).…”

Section: A Data Collectionmentioning

confidence: 99%

Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI

Dikici

Ryu

Demirer

et al. 2020

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

Brain Metastases (BM) complicate 20-40% of cancer cases. BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. However, BM lesion detection remains challenging partly due to their structural similarities to normal structures (e.g., vasculature). We propose a BMdetection framework using a single-sequence gadoliniumenhanced T1-weighted 3D MRI dataset. The framework focuses on detection of smaller (< 15 mm) BM lesions and consists of: (1) candidate-selection stage, using Laplacian of Gaussian approach for highlighting parts of a MRI volume holding higher BM occurrence probabilities, and (2) detection stage that iteratively processes cropped region-of-interest volumes centered by candidates using a custom-built 3D convolutional neural network ("CropNet"). Data is augmented extensively during training via a pipeline consisting of random gamma correction and elastic deformation stages; the framework thereby maintains its invariance for a plausible range of BM shape and intensity representations. This approach is tested using five-fold cross-validation on 217 datasets from 158 patients, with training and testing groups randomized per patient to eliminate learning bias. The BM database included lesions with a mean diameter of ~5.4 mm and a mean volume of ~160 mm 3 . For 90% BM-detection sensitivity, the framework produced on average 9.12 falsepositive BM detections per patient (standard deviation of 3.49); for 85% sensitivity, the average number of falsepositives declined to 5.85. Comparative analysis showed that the framework produces comparable BM-detection accuracy with the state-of-art approaches validated for significantly larger lesions.Index Terms-magnetic resonance imaging, brain metastases, convolutional neural networks, deep learning, scale-space representations, computer-aided detection, medical image analysis.

show abstract

“…Interfacing AI techniques with real-time data through a graphical user interface has the potential to lighten the radiologist's burden of image data curation, annotations, segmentation, and risk prediction (167). AI tools such as qQuant, ImageJ, and RADSpa can help radiologists access and characterize malignancy in tissues.…”

Section: Interface Validation and Extensionsmentioning

confidence: 99%

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Saba¹,

Skandha²,

Gupta³

et al. 2021

Ann Transl Med

View full text Add to dashboard Cite

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type

show abstract

A User Interface for Optimizing Radiologist Engagement in Image Data Curation for Artificial Intelligence

Cited by 26 publications

References 15 publications

Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Contact Info

Product

Resources

About