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Fully automatic scheme for measuring liver volume in 3D MR images
Trong-Ngoc Le1,2, Pham The Bao2, Hieu Trung Huynh1
1Faculty of Information Technology, Industrial University of Ho Chi Minh City, Viet Nam.
Bio-Medical Materials and Engineering
|September 26, 2015
Summary
A new automatic scheme accurately measures liver volume using 3D MRI scans. This automated method significantly reduces measurement time compared to manual tracing, offering a highly accurate and efficient solution for liver volumetry.
Area of Science:
- Medical Imaging
- Radiology
- Computational Anatomy
Background:
- Accurate liver volume measurement is crucial for clinical diagnosis and treatment planning.
- Manual liver volumetry is time-consuming and prone to inter-observer variability.
- Developing automated methods for liver volume assessment is essential for improving efficiency and consistency.
Purpose of the Study:
- To develop and validate a fully automatic scheme for liver volume measurement in 3D Magnetic Resonance Imaging (MRI).
- To compare the accuracy and efficiency of the proposed automatic method against manual tracing by radiologists.
Main Methods:
- A four-stage automated process involving image preprocessing, histogram analysis with neural network peak detection, thresholding, and image refinement.
- Application of morphological and connected component operations to generate a rough liver shape.
- Refinement of liver boundaries using a 3D geodesic-active-contour segmentation algorithm.
Main Results:
- The automatic liver volumetry achieved excellent agreement with manual measurements, indicated by a Dice overlap coefficient of 91.0 ±2.8% and average accuracy of 99.0 ±0.4%.
- The mean processing time for the automatic scheme was significantly reduced to 1.02 ±0.08 minutes, compared to 24.3 ±3.7 minutes for manual volumetry (p < 0.001).
Conclusions:
- The developed automatic MRI liver volumetry scheme provides a highly accurate and efficient method for measuring liver volume.
- This automated approach offers a substantial improvement over manual tracing in terms of speed and consistency, with potential for widespread clinical application.
