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Optimization of Multi-Atlas Segmentation with Joint Label Fusion Algorithm for Automatic Segmentation in Prostate MR Imaging

Investigative Magnetic Resonance Imaging 2020년 24권 3호 p.123 ~ 131
최윤호, 김재훈, 김찬교,
소속 상세정보
최윤호 ( Choi Yoon-Ho ) - Sungkyunkwan University SAIHST Department of Health Sciences and Technology
김재훈 ( Kim Jae-Hun ) - Sungkyunkwan University School of Medicine Samsung Medical Center Department of Radiology
김찬교 ( Kim Chan-Kyo ) - Sungkyunkwan University School of Medicine Samsung Medical Center Department of Radiology

Abstract


Purpose: Joint label fusion (JLF) is a popular multi-atlas-based segmentation algorithm, which compensates for dependent errors that may exist between atlases. However, in order to get good segmentation results, it is very important to set the several free parameters of the algorithm to optimal values. In this study, we first investigate the feasibility of a JLF algorithm for prostate segmentation in MR images, and then suggest the optimal set of parameters for the automatic prostate segmentation by validating the results of each parameter combination.

Materials and Methods: We acquired T2-weighted prostate MR images from 20 normal heathy volunteers and did a series of cross validations for every set of parameters of JLF. In each case, the atlases were rigidly registered for the target image. Then, we calculated their voting weights for label fusion from each combination of JLF’s parameters (rpxy, rpz, rsxy, rsz, β). We evaluated the segmentation performances by five validation metrics of the Prostate MR Image Segmentation challenge.

Results: As the number of voxels participating in the voting weight calculation and the number of referenced atlases is increased, the overall segmentation performance is gradually improved. The JLF algorithm showed the best results for dice similarity coefficient, 0.8495 ± 0.0392; relative volume difference, 15.2353 ± 17.2350; absolute relative volume difference, 18.8710 ± 13.1546; 95% Hausdorff distance, 7.2366 ± 1.8502; and average boundary distance, 2.2107 ± 0.4972; in parameters of rpxy = 10, rpz = 1, rsxy = 3, rsz = 1, and β = 3.

Conclusion: The evaluated results showed the feasibility of the JLF algorithm for automatic segmentation of prostate MRI. This empirical analysis of segmentation results by label fusion allows for the appropriate setting of parameters.

키워드

Prostate; Magnetic resonance imaging; Multi-atlas based segmentation; Joint label fusion; Automatic prostate segmentation

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