Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies

Refael Vivanti*, Leo Joskowicz, Naama Lev-Cohain, Ariel Ephrat, Jacob Sosna

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Radiological longitudinal follow-up of tumors in CT scans is essential for disease assessment and liver tumor therapy. Currently, most tumor size measurements follow the RECIST guidelines, which can be off by as much as 50%. True volumetric measurements are more accurate but require manual delineation, which is time-consuming and user-dependent. We present a convolutional neural networks (CNN) based method for robust automatic liver tumor delineation in longitudinal CT studies that uses both global and patient specific CNNs trained on a small database of delineated images. The inputs are the baseline scan and the tumor delineation, a follow-up scan, and a liver tumor global CNN voxel classifier built from radiologist-validated liver tumor delineations. The outputs are the tumor delineations in the follow-up CT scan. The baseline scan tumor delineation serves as a high-quality prior for the tumor characterization in the follow-up scans. It is used to evaluate the global CNN performance on the new case and to reliably predict failures of the global CNN on the follow-up scan. High-scoring cases are segmented with a global CNN; low-scoring cases, which are predicted to be failures of the global CNN, are segmented with a patient-specific CNN built from the baseline scan. Our experimental results on 222 tumors from 31 patients yield an average overlap error of 17% (std = 11.2) and surface distance of 2.1 mm (std = 1.8), far better than stand-alone segmentation. Importantly, the robustness of our method improved from 67% for stand-alone global CNN segmentation to 100%. Unlike other medical imaging deep learning approaches, which require large annotated training datasets, our method exploits the follow-up framework to yield accurate tumor tracking and failure detection and correction with a small training dataset. [Figure not available: see fulltext.].

Original languageAmerican English
Pages (from-to)1699-1713
Number of pages15
JournalMedical and Biological Engineering and Computing
Volume56
Issue number9
DOIs
StatePublished - 1 Sep 2018

Bibliographical note

Funding Information:
Acknowledgements This work was partially supported by Grant 53681 from the Israel Ministry of Science, Technology and Space entitled: METASEG: a new medical image segmentation paradigm for clinical decision support and big data radiology.

Publisher Copyright:
© 2018, International Federation for Medical and Biological Engineering.

Keywords

  • Convolutional neural networks
  • Deep learning
  • Follow-up CT scans
  • Liver tumor segmentation
  • Longitudinal studies

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