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CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT

  • Bo Zhou
  • Adam P. HarrisonEmail author
  • Jiawen Yao
  • Chi-Tung Cheng
  • Jing Xiao
  • Chien-Hung Liao
  • Le Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)

Abstract

As the demand for more descriptive machine learning models grows within medical imaging, bottlenecks due to data paucity will exacerbate. Thus, collecting enough large-scale data will require automated tools to harvest data/label pairs from messy and real-world datasets, such as hospital picture archiving and communication systems (PACSs). This is the focus of our work, where we present a principled data curation tool to extract multi-phase computed tomography (CT) liver studies and identify each scan’s phase from a real-world and heterogenous hospital PACS dataset. Emulating a typical deployment scenario, we first obtain a set of noisy labels from our institutional partners that are text mined using simple rules from DICOM tags. We train a deep learning system, using a customized and streamlined 3D squeeze and excitation (SE) architecture, to identify non-contrast, arterial, venous, and delay phase dynamic CT liver scans, filtering out anything else, including other types of liver contrast studies. To exploit as much training data as possible, we also introduce an aggregated cross entropy loss that can learn from scans only identified as “contrast”. Extensive experiments on a dataset of 43K scans of 7680 patient imaging studies demonstrate that our 3DSE architecture, armed with our aggregated loss, can achieve a mean F1 of 0.977 and can correctly harvest up to \(92.7\%\) of studies, which significantly outperforms the text-mined and standard-loss approach, and also outperforms other, and more complex, model architectures.

Keywords

Data curation PACS Dynamic CT Phase recognition 

Supplementary material

490967_1_En_16_MOESM1_ESM.pdf (499 kb)
Supplementary material 1 (pdf 499 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bo Zhou
    • 1
    • 2
  • Adam P. Harrison
    • 2
    Email author
  • Jiawen Yao
    • 2
  • Chi-Tung Cheng
    • 4
  • Jing Xiao
    • 3
  • Chien-Hung Liao
    • 4
  • Le Lu
    • 2
  1. 1.Biomedical EngineeringYale UniversityNew HavenUSA
  2. 2.PAII Inc.BethesdaUSA
  3. 3.PingAn TechnologyShenzhenChina
  4. 4.Chang Gung Memorial HospitalLinkouROC

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