Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans.

Usman Mahmood,Christopher Kanan,Lorenzo Mannelli,David D B Bates,Giuseppe Corrias,Yusuf E Erdi

doi:10.3390/diagnostics12030672

Usman Mahmood, Christopher Kanan + Show 4 more

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https://doi.org/10.3390/diagnostics12030672

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Abstract

We map single energy CT (SECT) scans to synthetic dual-energy CT (synth-DECT) material density iodine (MDI) scans using deep learning (DL) and demonstrate their value for liver segmentation. A 2D pix2pix (P2P) network was trained on 100 abdominal DECT scans to infer synth-DECT MDI scans from SECT scans. The source and target domain were paired with DECT monochromatic 70 keV and MDI scans. The trained P2P algorithm then transformed 140 public SECT scans to synth-DECT scans. We split 131 scans into 60% train, 20% tune, and 20% held-out test to train four existing liver segmentation frameworks. The remaining nine low-dose SECT scans tested system generalization. Segmentation accuracy was measured with the dice coefficient (DSC). The DSC per slice was computed to identify sources of error. With synth-DECT (and SECT) scans, an average DSC score of () and () was achieved on the held-out and generalization test sets. Synth-DECT-trained systems required less data to perform as well as SECT-trained systems. Low DSC scores were primarily observed around the scan margin or due to non-liver tissue or distortions within ground-truth annotations. In general, training with synth-DECT scans resulted in improved segmentation performance with less data.

Highlights

Publisher’s Note: MDPI stays neutralThe automatic segmentation of the liver and associated tumors from single energy computed tomography (SECT) exams remains a challenge because of limited training data and overlapping intensity values of tissues or materials with different elemental compositions [1,2]
We evaluate the quality of the mapping from dual-energy CT (DECT) 70 keV VMI to the synth-DECT
We evaluate our hypothesis that systems trained using the synth-DECT material density iodine (MDI) scan types enable generalization with limited data

Summary

Introduction

The automatic segmentation of the liver and associated tumors from single energy computed tomography (SECT) exams remains a challenge because of limited training data and overlapping intensity values of tissues or materials with different elemental compositions [1,2]. Most deep learning(DL)-based segmentation systems use object-level models that disregard the influence of tissues with different compositions (i.e., iodine-rich blood vessels or organs) [2,3]. With SECT scans, it is technically challenging to identify or classify tissue composition strictly based on the intensity measurement or CT Hounsfield unit (HU) [1,3]. With dual-energy CT (DECT), the differential attenuation properties of tissues at low and high X-ray energies are exploited to differentiate and quantify material composition [1,3] and generate multiple image types. DECT material density (MD) images display the concentration of specific elements such as iodine (MDI). Each of the image types provides a with regard to jurisdictional claims in published maps and institutional affiliations

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