Abstract
Pancreas CT segmentation offers promise at understanding the structural manifestation of metabolic conditions. To date, the medical primary record of conditions that impact the pancreas is in the electronic health record (EHR) in terms of diagnostic phenotype data (e.g., ICD-10 codes). We posit that similar structural phenotypes could be revealed by studying subjects with similar medical outcomes. Segmentation is mainly driven by imaging data, but this direct approach may not consider differing canonical appearances with different underlying conditions (e.g., pancreatic atrophy versus pancreatic cysts). To this end, we exploit clinical features from EHR data to complement image features for enhancing the pancreas segmentation, especially in high-risk outcomes. Specifically, we propose, to the best of our knowledge, the first phenotype embedding model for pancreas segmentation by predicting representatives that share similar comorbidities. Such an embedding strategy can adaptively refine the segmentation outcome based on the discriminative contexts distilled from clinical features. Experiments with 2000 patients’ EHR data and 300 CT images with the healthy pancreas, type II diabetes, and pancreatitis subjects show that segmentation by predictive phenotyping significantly improves performance over state-of-the-arts (Dice score 0.775 to 0.791, \(p < 0.05\), Wilcoxon signed-rank test). The proposed method additionally achieves superior performance on two public testing datasets, BTCV MICCAI Challenge 2015 and TCIA pancreas CT. Our approach provides a promising direction of advancing segmentation with phenotype features while without requiring EHR data as input during testing.
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Acknowledgements
This research is supported by NIH Common Fund and National Institute of Diabetes, Digestive and Kidney Diseases U54DK120058, NSF CAREER 1452485, NIH grants, 2R01EB006136, 1R01EB017230 (Landman), and R01NS09529. The identified datasets used for the analysis described were obtained from the Research Derivative (RD), database of clinical and related data. The imaging dataset(s) used for the analysis described were obtained from ImageVU, a research repository of medical imaging data and image-related metadata. ImageVU and RD are supported by the VICTR CTSA award (ULTR000445 from NCATS/NIH) and Vanderbilt University Medical Center institutional funding. ImageVU pilot work was also funded by PCORI (contract CDRN-1306-04869).
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Tang, Y. et al. (2021). Pancreas CT Segmentation by Predictive Phenotyping. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12901. Springer, Cham. https://doi.org/10.1007/978-3-030-87193-2_3
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