Dr. David A. A. Cook Profile

Dr. David A. A. Cook

at Mayo Clinic

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Author

Publications (4)

Proceedings Article | 3 April 2023 Presentation + Paper

A training program to reduce reader search errors for liver metastasis detection in CT

Scott Hsieh, Akitoshi Inoue, Mariana Yalon, David Cook, Jeff Fidler, Hao Gong, Parvathy Sudhir Pillai, Andrew Vercnocke, Matthew Johnson, Shuai Leng, Lifeng Yu, David Holmes, Rickey Carter, Cynthia McCollough, Joel Fletcher

Proceedings Volume 12467, 1246704 (2023) https://doi.org/10.1117/12.2654007

KEYWORDS: Education and training, Liver, Cancer detection, Eye tracking, Eye, Error analysis, Calibration

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SPIE Journal Paper | 13 September 2022

Individualized and generalized models for predicting observer performance on liver metastasis detection using CT

Parvathy Sudhir Pillai, David Holmes, Rickey Carter, Akitoshi Inoue, David Cook, Ron Karwoski, Jeff Fidler, Joel Fletcher, Shuai Leng, Lifeng Yu, Cynthia McCollough, Scott Hsieh

JMI, Vol. 9, Issue 05, 055501, (September 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.5.055501

KEYWORDS: Performance modeling, Liver, Computed tomography, Feature extraction, Data modeling, 3D modeling, Radiology, RGB color model, Reconstruction algorithms, Convolutional neural networks

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Purpose: Radiologists exhibit wide inter-reader variability in diagnostic performance. This work aimed to compare different feature sets to predict if a radiologist could detect a specific liver metastasis in contrast-enhanced computed tomography (CT) images and to evaluate possible improvements in individualizing models to specific radiologists.Approach: Abdominal CT images from 102 patients, including 124 liver metastases in 51 patients were reconstructed at five different kernels/doses using projection domain noise insertion to yield 510 image sets. Ten abdominal radiologists marked suspected metastases in all image sets. Potentially salient features predicting metastasis detection were identified in three ways: (i) logistic regression based on human annotations (semantic), (ii) random forests based on radiologic features (radiomic), and (iii) inductive derivation using convolutional neural networks (CNN). For all three approaches, generalized models were trained using metastases that were detected by at least two radiologists. Conversely, individualized models were trained using each radiologist’s markings to predict reader-specific metastases detection.Results: In fivefold cross-validation, both individualized and generalized CNN models achieved higher area under the receiver operating characteristic curves (AUCs) than semantic and radiomic models in predicting reader-specific metastases detection ability (p < 0.001). The individualized CNN with an AUC of mean (SD) 0.85(0.04) outperformed the generalized one [AUC = 0.78 ( 0.06 ) , p = 0.004]. The individualized semantic [AUC = 0.70 ( 0.05 ) ] and radiomic models [AUC = 0.68 ( 0.06 ) ] outperformed the respective generalized versions [semantic AUC = 0.66 ( 0.03 ) , p = 0.009; radiomic AUC = 0.64 ( 0.06 ) , p = 0.03].Conclusions: Individualized models slightly outperformed generalized models for all three feature sets. Inductive CNNs were better at predicting metastases detection than semantic or radiomic features. Generalized models have implementation advantages when individualized data are unavailable.

Proceedings Article | 4 April 2022 Presentation + Paper

Implementation and initial experience with an interactive eye-tracking system for measuring radiologists' visual search in diagnostic tasks using volumetric CT images

Hao Gong, Scott Hsieh, David Holmes, David Cook, Akitoshi Inoue, David Bartlett, Francis Baffour, Hiroaki Takahashi, Shuai Leng, Lifeng Yu, Joel Fletcher, Cynthia McCollough

Proceedings Volume 12031, 120310Q (2022) https://doi.org/10.1117/12.2611808

KEYWORDS: Visualization, Diagnostics, Liver, Computed tomography, Cameras, X-ray computed tomography, Head, Target detection, Radiology, Manufacturing

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Proceedings Article | 4 April 2022 Presentation + Paper

A 25-reader performance study for hepatic metastasis detection: lessons from unsupervised learning

Scott Hsieh, Akitoshi Inoue, Parvathy Sudhir Pillai, Hao Gong, David Holmes, David Cook, Shuai Leng, Lifeng Yu, Rickey Carter, Joel Fletcher, Cynthia McCollough

Proceedings Volume 12031, 1203116 (2022) https://doi.org/10.1117/12.2611543

KEYWORDS: Machine learning, Liver, Visualization, MATLAB, Abdomen, Temporal resolution, Spatial resolution, Scanners, Radiology

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