A hybrid segmentation method for partitioning the liver based on 4D DCE-MR images

Tian Zhang, Zhiyi Wu, Jurgen H. Runge, Cristina Lavini, Jaap Stoker, Thomas Van Gulik, Kasia P. Cieslak, Lucas J. Van Vliet, Frans M. Vos

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
55 Downloads (Pure)

Abstract

The Couinaud classification of hepatic anatomy partitions the liver into eight functionally independent segments. Detection and segmentation of the hepatic vein (HV), portal vein (PV) and inferior vena cava (IVC) plays an important role in the subsequent delineation of the liver segments. To facilitate pharmacokinetic modeling of the liver based on the same data, a 4D DCE-MR scan protocol was selected. This yields images with high temporal resolution but low spatial resolution. Since the liver's vasculature consists of many tiny branches, segmentation of these images is challenging. The proposed framework starts with registration of the 4D DCE-MRI series followed by region growing from manually annotated seeds in the main branches of key blood vessels in the liver. It calculates the Pearson correlation between the time intensity curves (TICs) of a seed and all voxels. A maximum correlation map for each vessel is obtained by combining the correlation maps for all branches of the same vessel through a maximum selection per voxel. The maximum correlation map is incorporated in a level set scheme to individually delineate the main vessels. Subsequently, the eight liver segments are segmented based on three vertical intersecting planes fit through the three skeleton branches of HV and IVC's center of mass as well as a horizontal plane fit through the skeleton of PV. Our segmentation regarding delineation of the vessels is more accurate than the results of two state-of-the-art techniques on five subjects in terms of the average symmetric surface distance (ASSD) and modified Hausdorff distance (MHD). Furthermore, the proposed liver partitioning achieves large overlap with manual reference segmentations (expressed in Dice Coefficient) in all but a small minority of segments (mean values between 87% and 94% for segments 2-8). The lower mean overlap for segment 1 (72%) is due to the limited spatial resolution of our DCE-MR scan protocol.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImage Processing
PublisherSPIE
Volume10574
ISBN (Electronic)9781510616370
DOIs
Publication statusPublished - 2018
EventMedical Imaging 2018: Ultrasonic Imaging and Tomography - Houston, United States
Duration: 10 Feb 201815 Feb 2018

Conference

ConferenceMedical Imaging 2018: Ultrasonic Imaging and Tomography
CountryUnited States
CityHouston
Period10/02/1815/02/18

Keywords

  • Couinaud classification
  • DCE-MRI
  • Functional liver segments
  • level set
  • time intensity curve (TIC)

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