Abstract
This study aims to develop a semiautomated pipeline and user interface (LiVaS) for rapid segmentation and labeling of MRI liver vasculature and evaluate its time efficiency and accuracy against manual reference standard. Retrospective feasibility pilot study. Liver MR images from different scanners from 36 patients were included, and 4/36 patients were randomly selected for manual segmentation as referenced standard. The liver was segmented in each contrast phase and masks registered to the pre-contrast segmentation. Voxel-wise signal trajectories were clustered using the k-means algorithm. Voxel clusters that best segment the liver vessels were selected and labeled by three independent radiologists and a research scientist using LiVaS. Segmentation times were compared using a paired-sample t-test on log-transformed data. The agreement was analyzed qualitatively and quantitatively using DSC for hepatic and portal vein segmentations. The mean segmentation time among four readers was significantly shorter than manual (3.6 ± 1.4 vs. 70.0 ± 29.2 min; p < 0.001), even when using a higher number of clusters to enhance accuracy. The DSC for portal and hepatic veins reached up to 0.69 and 0.70, respectively. LiVaS segmentations were overall of good quality, with variations in performance related to the presence/severity of liver disease, acquisition timing, and image quality. Our semi-automated pipeline was robust to different MRI vendors in producing segmentation and labeling of liver vasculature in agreement with expert manual annotations, with significantly higher time efficiency. LiVaS could facilitate the creation of large, annotated datasets for training and validation of neural networks for automated MRI liver vascularity segmentation.
Highlights
Key Finding: In this pilot feasibility study, our semiautomated pipeline for segmentation of liver vascularity (LiVaS) on MR images produced segmentations with simultaneous labeling of portal and hepatic veins in good agreement with the manual reference standard but at significantly shorter times (mean LiVaS 3.6 ± 1.4 vs. mean manual 70.0 ± 29.2 min; p < 0.001).
Importance: LiVaS was robust in producing liver MRI vascular segmentations across images from different scanners in agreement with expert manual annotations, with significant ly higher time efficiency, and therefore potential scalability.
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Data Availability
Study data can be provided at reasonable request to the corresponding author. LiVaS code including the UI is publicly available at https://zenodo.org/record/7989974.
Abbreviations
- AP:
-
Arterial phase
- DSC:
-
Dice similarity coefficient
- HA:
-
Hepatic arteries
- HV:
-
Hepatic veins
- MRI:
-
Magnetic resonance imaging
- PV:
-
Portal veins
- UI:
-
User interface
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Funding
This work has been primarily funded by the RSNA R&E Foundation Fellow Grant 04 (RF2104). Partial financial support to one of the coauthors (MZ) was also provided by the NIH grant P50CA228944.
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All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed in collaboration by all authors. The first draft of the manuscript was written by Mladen Zecevic and Kyle Hasentab, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Zecevic, M., Hasenstab, K.A., Wang, K. et al. Signal Intensity Trajectories Clustering for Liver Vasculature Segmentation and Labeling (LiVaS) on Contrast-Enhanced MR Images: A Feasibility Pilot Study. J Digit Imaging. Inform. med. 37, 873–883 (2024). https://doi.org/10.1007/s10278-024-00970-w
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DOI: https://doi.org/10.1007/s10278-024-00970-w