FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI
FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI
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DOI：
10.1002/mrm.28022
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发表时间：
2019-10-21
影响因子：
3.3
通讯作者：
Reuter, Martin
中科院分区：
文献类型：
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作者：
Estrada, Santiago;Lu, Ran;Reuter, Martin
引入并验证一种新型、快速、全自动的深度学习管道（FatSegNet），在Dixon MRI扫描的一致的、解剖定义的腹部区域内准确识别、分割和量化内脏和皮下脂肪组织（VAT和SAT）。FatSegNet由三个阶段组成：(a)使用两个2d竞争密集全卷积网络（CDFNet）对腹部区域进行一致定位，(b)使用独立的CDFNets在三个视图上对脂肪组织进行分割，以及(c)视图聚合。FatSegNet通过以下方式得到验证：(1)分割准确性的比较（六倍交叉验证），(2)测试-重测信度，(3)对随机选择的人工重新编辑病例的概括性，以及(4)在莱茵兰研究（一个大型前瞻性人群队列）中复制年龄和性别效应。结果与传统深度学习网络相比，CDFNet具有更高的准确性和鲁棒性。FatSegNet Dice得分在VAT上优于手动评分者（0.850比0.788），在SAT上产生类似的结果（0.975比0.982）。该管道在测试复测（ICC VAT 0.998和SAT 0.996）和手动重新编辑（ICC VAT 0.999和SAT 0.999）方面具有出色的一致性。结论：FatSegNet可以很好地推广到不同的体型，在大型队列研究中敏感地复制已知的VAT和SAT体积效应，并允许对脂肪区室进行局部分析。此外，它可以在大约1分钟内可靠地分析3D Dixon MRI，为Rhineland研究中的腹部脂肪组织分析提供了高效且经过验证的管道。
Purpose Introduce and validate a novel, fast, and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify visceral and subcutaneous adipose tissue (VAT and SAT) within a consistent, anatomically defined abdominal region on Dixon MRI scans. Methods FatSegNet is composed of three stages: (a) Consistent localization of the abdominal region using two 2D-Competitive Dense Fully Convolutional Networks (CDFNet), (b) Segmentation of adipose tissue on three views by independent CDFNets, and (c) View aggregation. FatSegNet is validated by: (1) comparison of segmentation accuracy (sixfold cross-validation), (2) test-retest reliability, (3) generalizability to randomly selected manually re-edited cases, and (4) replication of age and sex effects in the Rhineland Study-a large prospective population cohort. Results The CDFNet demonstrates increased accuracy and robustness compared to traditional deep learning networks. FatSegNet Dice score outperforms manual raters on VAT (0.850 vs. 0.788) and produces comparable results on SAT (0.975 vs. 0.982). The pipeline has excellent agreement for both test-retest (ICC VAT 0.998 and SAT 0.996) and manual re-editing (ICC VAT 0.999 and SAT 0.999). Conclusions FatSegNet generalizes well to different body shapes, sensitively replicates known VAT and SAT volume effects in a large cohort study and permits localized analysis of fat compartments. Furthermore, it can reliably analyze a 3D Dixon MRI in similar to 1 minute, providing an efficient and validated pipeline for abdominal adipose tissue analysis in the Rhineland Study.