[Frontiers in Bioscience, Landmark, 25, 1746-1764, June 1, 2020]

Machine learning paradigm for dynamic contrast-enhanced MRI evaluation of expanding bladder

Dee H. Wu1, Zhongning Chen2, Justin C. North1, Mainak Biswas2, Jonathan Vo1, Jasjit S. Suri4

1Radiological Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA, 2University of Arkansas for Medical Sciences, AR, USA, 3JIS University, Agarpara, Kolkata, India, 4Advanced Knowledge Engineering Center, Global Biomedical Technologies, Inc., CA, USA and Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA

TABLE OF CONTENTS

1. Abstract
2. Introduction
3. Materials and methods
4. Results
5. Discussion
6. Conclusions
7. Acknowledgments
8. References

1. ABSTRACT

Delineation of the bladder under a dynamic contrast enhanced (DCE)-MRI protocol requires robust segmentation. However, this method is subject to errors due to variations in the content of fluid within the bladder, as well as presence of air and similarity of signal intensity in adjacent organs. Introduction of the contrast media into the bladder also causes signal errors due to alterations in the shape of the bladder. To circumvent such errors, and to improve the accuracy, we adapted a machine learning paradigm that utilizes the global bladder shape. The ML system first uses the combination of low level image processing tools such as filtering, and mathematical morphology as preprocessing step. We use neural network for training the network using extracted features and application of trained model on test slices to compute the delineated bladder shapes. This ML-based integrated system has an accuracy of 90.73% and time reduction of 65.2% in over manual delineation and can be used in clinical settings for IC/BPS patient care. Finally, we apply Jaccard Similarity Measure which we report to have a mean score of 0.933 (95% Confidence Interval 0.923, 0.944)

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Abbreviations: Dynamic Contrast Enhanced: DCE, Magnetic Resonance Imaging: MRI, Interstitial Cystitis: IC, Bladder Pain Syndrome: BPS, Machine Learning: ML, Ultrasound: US, DTPA) Gadolinium diethylenetriaminepentacetate: GD, Region of Interest: ROI, Menger Curvature: MC, Dot Product Angle: DPA, Maximum first order difference of the curvature: MDC, Second Order numerical differential estimate of the angle: SOA

Key Words: Machine Learning, Image Segmentation, Hollow Organs, Rim Contrast Enhancement

Send correspondence to: Dee H. Wu, Department of Radiological Sciences, Nicholson Tower Room 3910, 940 NE 13th Street, Stanton L. Young, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, Tel: 405-271-8001 Fax: 405-271-3462 E-mail: dee-wu@ouhsc.edu