Deformable image registration based on single or multi-atlas methods for automatic muscle segmentation and the generation of augmented imaging datasets

被引:4
作者
Henson, William [1 ,2 ]
Mazza, Claudia [1 ,2 ]
Dall'Ara, Enrico [2 ,3 ]
机构
[1] Univ Sheffield, Dept Mech Engn, Sheffield, England
[2] Univ Sheffield, INSIGNEO Inst Sil Med, Sheffield, England
[3] Univ Sheffield, Dept Oncol & Metab, Sheffield, England
来源
PLOS ONE | 2023年 / 18卷 / 03期
基金
英国工程与自然科学研究理事会;
关键词
LOWER-LIMB; MRI; TISSUE;
D O I
10.1371/journal.pone.0273446
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Muscle segmentation is a process relied upon to gather medical image-based muscle characterisation, useful in directly assessing muscle volume and geometry, that can be used as inputs to musculoskeletal modelling pipelines. Manual or semi-automatic techniques are typically employed to segment the muscles and quantify their properties, but they require significant manual labour and incur operator repeatability issues. In this study an automatic process is presented, aiming to segment all lower limb muscles from Magnetic Resonance (MR) imaging data simultaneously using three-dimensional (3D) deformable image registration (single inputs or multi-atlas). Twenty-three of the major lower limb skeletal muscles were segmented from five subjects, with an average Dice similarity coefficient of 0.72, and average absolute relative volume error (RVE) of 12.7% (average relative volume error of -2.2%) considering the optimal subject combinations. The multi-atlas approach showed slightly better accuracy (average DSC: 0.73; average RVE: 1.67%). Segmented MR imaging datasets of the lower limb are not widely available in the literature, limiting the potential of new, probabilistic methods such as deep learning to be used in the context of muscle segmentation. In this work, Non-linear deformable image registration is used to generate 69 manually checked, segmented, 3D, artificial datasets, allowing access for future studies to use these new methods, with a large amount of reliable reference data.
引用
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页数:19
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