Vessel segmentation from abdominal magnetic resonance images: adaptive and reconstructive approach

Abstract

The liver vessels, which have low signal and run next to brighter bile ducts, are difficult to segment from MR images. This study presents a fully automated and adaptive method to segment portal and hepatic veins on magnetic resonance images. In the proposed approach, segmentation of these vessels is achieved in four stages: (i) initial segmentation, (ii) refinement, (iii) reconstruction, and (iv) post-processing. In the initial segmentation stage, k-means clustering is used, the results of which are refined iteratively with linear contrast stretching algorithm in the next stage, generating a mask image. In the reconstruction stage, vessel regions are reconstructed with the marker image from the first stage and the mask image from the second stage. Experimental data sets include slices that show fat tissues, which have the same gray level values with vessels, outside the margin of the liver. These structures are removed in the last stage. Results show that the proposed approach is more efficient than other thresholding-based methods. Copyright © 2016 John Wiley & Sons, Ltd.

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

“…In [23], k-means clustering is used for rough liver vessel segmentation. Further iterative refinement steps based on morphological operations are applied to refine the segmentation.…”

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Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning

“…Nevertheless, Bruyninckx et al [28] segmented the whole portal vein including peripheral branches and obtained Dice coefficient from 0.53 to 0.68. Recently, Goceri et al [31] reported Dice coefficient from 0.51 to 0.62 for segmentation of portal vein from MR images. The previous work emphasized that PV segmentation is compromised by image noise and the presence of hepatic arteries and other veins.…”

Reconstruction of coronary circulation networks: A review of methods

“…Unsupervised classifiers are based on the statistical properties of the input data of specific image features. Examples include principal component analysis, K‐means clustering, and Fuzzy C‐means clustering …”