Detecting pancreatic adenocarcinoma in multi-phase CT scans via alignment ensemble

Yingda Xia, Qihang Yu, Wei Shen, Yuyin Zhou, Elliot K. Fishman, Alan L. Yuille

Research output: Contribution to journalArticlepeer-review

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among population. Screening for PDACs in dynamic contrast-enhanced CT is beneficial for early diagnose. In this paper, we investigate the problem of automated detecting PDACs in multi-phase (arterial and venous) CT scans. Multiple phases provide more information than single phase, but they are unaligned and inhomogeneous in texture, making it difficult to combine cross-phase information seamlessly. We study multiple phase alignment strategies, i.e., early alignment (image registration), late alignment (high-level feature registration) and slow alignment (multi-level feature registration), and suggest an ensemble of all these alignments as a promising way to boost the performance of PDAC detection. We provide an extensive empirical evaluation on two PDAC datasets and show that the proposed alignment ensemble significantly outperforms previous state-of-the-art approaches, illustrating strong potential for clinical use.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Mar 18 2020

Keywords

  • Feature alignment
  • Pancreatic tumor segmentation
  • PDAC detection

ASJC Scopus subject areas

  • General

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