13 March 2018 Automated segmentation and radiomic characterization of visceral fat on bowel MRIs for Crohn's disease
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Abstract
Crohn’s Disease is a relapsing and remitting disease involving chronic intestinal inflammation that is often characterized by hypertrophy of visceral adipose tissue (VAT). While an increased ratio of VAT to subcutaneous fat (SQF) has previously been identified as a predictor of worse outcomes in Crohn’s Disease, bowel-proximal fat regions have also been hypothesized to play a role in inflammatory response. However, there has been no detailed study of VAT and SQF regions on MRI to determine their potential utility in assessing Crohn’s Disease severity or guiding therapy. In this paper we present a fully-automated algorithm to segment and quantitatively characterize VAT and SQF via routinely acquired diagnostic bowel MRIs. Our automated segmentation scheme for VAT and SQF regions involved a combination of morphological processing and connected component analysis, and demonstrated DICE overlap scores of 0.86±0.05 and 0.91±0.04 respectively, when compared against expert annotations. Additionally, VAT regions proximal to the bowel wall (on diagnostic bowel MRIs) demonstrated a statistically significantly, higher expression of four unique radiomic features in pediatric patients with moderately active Crohn’s Disease. These features were also able to accurately cluster patients who required aggressive biologic therapy within a year of diagnosis from those who did not, with 87.5% accuracy. Our findings indicate that quantitative radiomic characterization of visceral fat regions on bowel MRIs may be highly relevant for guiding therapeutic interventions in Crohn’s Disease.
Conference Presentation
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Iulia Barbur, Iulia Barbur, Jacob Kurowski, Jacob Kurowski, Kaustav Bera, Kaustav Bera, Rajat Thawani, Rajat Thawani, Jean-Paul Achkar, Jean-Paul Achkar, Claudio Fiocchi, Claudio Fiocchi, Marsha Kay, Marsha Kay, Rishi Gupta, Rishi Gupta, Satish Viswanath, Satish Viswanath, } "Automated segmentation and radiomic characterization of visceral fat on bowel MRIs for Crohn's disease", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 1057615 (13 March 2018); doi: 10.1117/12.2293533; https://doi.org/10.1117/12.2293533
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