Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session F15: Artificial Intelligence, Machine Learning, and Data Science in Medicine and Biomedicine
11:30 AM–1:54 PM,
Tuesday, March 16, 2021
Sponsoring
Units:
GMED GDS
Chair: Michael Boss, American College of Radiology; Jie Ren, Merck & Co.
Abstract: F15.00008 : Framework for Assessing the Impact of CNN-based Image Segmentation on Multi-step Biomarker Extraction*
1:42 PM–1:54 PM
On Demand
Presenter:
Daniel Huff
(Department of Medical Physics, University of Wisconsin - Madison)
Authors:
Daniel Huff
(Department of Medical Physics, University of Wisconsin - Madison)
Zan Klanecek
(Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia)
Andrej Studen
(Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia)
Robert Jeraj
(Department of Medical Physics, University of Wisconsin - Madison)
We employ a CNN to segment the bowel on CT, quantify inflammation using 18F-FDG PET histogram metrics, and perform sensitivity analysis to optimize the extracted metric for our clinical task. CNN performance is characterized by Dice similarity coefficient (DSC). Perturbation-based analysis is used to quantify the impact of segmentation error on PET histogram metrics. Area under the receiver operating characteristic curve (AUC) is used to optimize the PET histogram metric for our clinical task.
The CNN had a validation DSC of 0.87±0.06 (mean±sd) for bowel segmentation. PET histogram metrics were robust to small dilations, erosions, and spatial shifts. The 96th percentile of the PET histogram was determined to be the optimal biomarker for classifying patients with an AUC of 0.91.
The presented framework is a method by which CNN segmentation error can be accounted for in medical image analyses.
*Supported by the UWCCC (P30CA014520) and the NIH (T32CA009206).
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