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Updated: May 3, 2026
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Liver segmentation in MRI: A fully automatic method based on stochastic partitions
F López-Mir1, V Naranjo1, J Angulo2
1Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
Abstract:
There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 ± 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI.
Insights
A novel automated method for liver segmentation in magnetic resonance imaging (MRI) was developed using a stochastic watershed transform. This technique significantly improves accuracy for robust liver segmentation in medical images.
Area of Science:
- Medical Imaging
- Computer Vision
- Image Processing
Background:
- Fully automated liver segmentation in MRI is limited, hindering the use of MRI's advantages over CT.
- Accurate liver segmentation is crucial for medical diagnosis and treatment planning.
Purpose of the Study:
- To present a new, fully automated method for liver segmentation in MRI.
- To enhance segmentation accuracy using a novel stochastic watershed variant.
Main Methods:
- A marker-controlled watershed transform to reduce oversegmentation.
- A new variant of stochastic watershed to enhance image gradients.
- A final classifier to generate the liver mask.
Main Results:
- The method achieved a high Jaccard coefficient of 0.91 ± 0.02 on 17 datasets.
- Demonstrated robustness and improved accuracy compared to existing methods.
- Optimal parameters were tuned on a training dataset.
Conclusions:
- The proposed stochastic watershed variant is a robust tool for automatic liver segmentation in MRI.
- This method addresses the need for efficient and accurate automated liver segmentation in medical imaging.
