Shape–intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images
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Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images.
First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image to dynamically generate a subject-specific probabilistic atlas for the test image. The most likely liver region of the test image is further determined based on the generated atlas. A rough segmentation is obtained by a maximum a posteriori classification of probability map, and the final liver segmentation is produced by a shape–intensity prior level set in the most likely liver region. Our method is evaluated and demonstrated on 25 test CT datasets from our partner site, and its results are compared with two state-of-the-art liver segmentation methods. Moreover, our performance results on 10 MICCAI test datasets are submitted to the organizers for comparison with the other automatic algorithms.
Using the 25 test CT datasets, average symmetric surface distance is \(1.09 \pm 0.34\) mm (range 0.62–2.12 mm), root mean square symmetric surface distance error is \(1.72 \pm 0.46\) mm (range 0.97–3.01 mm), and maximum symmetric surface distance error is \(18.04 \pm 3.51\) mm (range 12.73–26.67 mm) by our method. Our method on 10 MICCAI test data sets ranks 10th in all the 47 automatic algorithms on the site as of July 2015. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our method is a promising tool to improve the efficiency of both techniques.
The applicability of the proposed method to some challenging clinical problems and the segmentation of the liver are demonstrated with good results on both quantitative and qualitative experimentations. This study suggests that the proposed framework can be good enough to replace the time-consuming and tedious slice-by-slice manual segmentation approach.
KeywordsActive shape model Statistical shape model Expectation maximization Atlas-based segmentation Level set segmentation
The authors would like to thank the anonymous reviewers for their valuable comments and help suggestions that greatly improved the papers quality. This work was supported in part by the National Natural Science Foundation of China under Grant No. 61571158; the Scientific Research Fund of Heilongjiang Provincial Education Department (No. 12541164), and the Nature Science Foundation of Heilongjiang Province of China (No. F2015005).
Compliance with ethical standards
Conflict of interest
There is no conflict of interest in this study.
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