Publication: An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data
Issued Date
2021-12-01
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14712342
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2-s2.0-85115847757
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Mahidol University
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SCOPUS
Bibliographic Citation
BMC Medical Imaging. Vol.21, No.1 (2021)
Suggested Citation
Kittichai Wantanajittikul, Pairash Saiviroonporn, Suwit Saekho, Rungroj Krittayaphong, Vip Viprakasit An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data. BMC Medical Imaging. Vol.21, No.1 (2021). doi:10.1186/s12880-021-00669-2 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/77521
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Title
An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data
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Abstract
Background: To estimate median liver iron concentration (LIC) calculated from magnetic resonance imaging, excluded vessels of the liver parenchyma region were defined manually. Previous works proposed the automated method for excluding vessels from the liver region. However, only user-defined liver region remained a manual process. Therefore, this work aimed to develop an automated liver region segmentation technique to automate the whole process of median LIC calculation. Methods: 553 MR examinations from 471 thalassemia major patients were used in this study. LIC maps (in mg/g dry weight) were calculated and used as the input of segmentation procedures. Anatomical landmark data were detected and used to restrict ROI. After that, the liver region was segmented using fuzzy c-means clustering and reduced segmentation errors by morphological processes. According to the clinical application, erosion with a suitable size of the structuring element was applied to reduce the segmented liver region to avoid uncertainty around the edge of the liver. The segmentation results were evaluated by comparing with manual segmentation performed by a board-certified radiologist. Results: The proposed method was able to produce a good grade output in approximately 81% of all data. Approximately 11% of all data required an easy modification step. The rest of the output, approximately 8%, was an unsuccessful grade and required manual intervention by a user. For the evaluation matrices, percent dice similarity coefficient (%DSC) was in the range 86–92, percent Jaccard index (%JC) was 78–86, and Hausdorff distance (H) was 14–28 mm, respectively. In this study, percent false positive (%FP) and percent false negative (%FN) were applied to evaluate under- and over-segmentation that other evaluation matrices could not handle. The average of operation times could be reduced from 10 s per case using traditional method, to 1.5 s per case using our proposed method. Conclusion: The experimental results showed that the proposed method provided an effective automated liver segmentation technique, which can be applied clinically for automated median LIC calculation in thalassemia major patients.