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Multi-scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma

  • Zhuotun ZhuEmail author
  • Yingda XiaEmail author
  • Lingxi Xie
  • Elliot K. Fishman
  • Alan L. Yuille
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans. Our idea is named multi-scale segmentation-for-classification, which classifies volumes by checking if at least a sufficient number of voxels is segmented as tumors, by which we can provide radiologists with tumor locations. In order to deal with tumors with different scales, we train and test our volumetric segmentation networks with multi-scale inputs in a coarse-to-fine flowchart. A post-processing module is used to filter out outliers and reduce false alarms. We collect a new dataset containing 439 CT scans, in which 136 cases were diagnosed with PDAC and 303 cases are normal, which is the largest set for PDAC tumors to the best of our knowledge. To offer the best trade-off between sensitivity and specificity, our proposed framework reports a sensitivity of \(94.1\%\) at a specificity of \(98.5\%\), which demonstrates the potential to make a clinical impact.

Keywords

PDAC Pancreas segmentation CT scan 

Notes

Acknowledgements

This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.The Johns Hopkins UniversityBaltimoreUSA
  2. 2.The Johns Hopkins University School of MedicineBaltimoreUSA

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