干旱区生态环境知识资源中心(Arid): CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation

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DOI	10.1016/j.neuroimage.2022.119703
	CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation
	Faber, Jennifer; Kuegler, David; Bahrami, Emad; Heinz, Lea-Sophie; Timmann, Dagmar; Ernst, Thomas M.; Deike-Hofmann, Katerina; Klockgether, Thomas; van de Warrenburg, Bart; van Gaalen, Judith; Reetz, Kathrin; Romanzetti, Sandro; Oz, Gulin; Joers, James M.; Diedrichsen, Jorn; Reuter, Martin
通讯作者	Reuter, M
来源期刊	NEUROIMAGE
ISSN	1053-8119
EISSN	1095-9572
出版年	2022
卷号	264
英文摘要	Quantifying the volume of the cerebellum and its lobes is of profound interest in various neurodegenerative and acquired diseases. Especially for the most common spinocerebellar ataxias (SCA), for which the first antisense oligonculeotide-base gene silencing trial has recently started, there is an urgent need for quantitative, sensitive imaging markers at pre-symptomatic stages for stratification and treatment assessment. This work introduces CerebNet, a fully automated, extensively validated, deep learning method for the lobular segmentation of the cerebellum, including the separation of gray and white matter. For training, validation, and testing, T1-weighted images from 30 participants were manually annotated into cerebellar lobules and vermal sub-segments, as well as cerebellar white matter. CerebNet combines FastSurferCNN, a UNet-based 2.5D segmentation network, with extensive data augmentation, e.g. realistic non-linear deformations to increase the anatomical variety, eliminating additional preprocessing steps, such as spatial normalization or bias field correction. CerebNet demonstrates a high accuracy (on average 0.87 Dice and 1.742mm Robust Hausdorff Distance across all structures) outperforming state-of-the-art approaches. Furthermore, it shows high test-retest reliability (average ICC > 0 . 97 on OASIS and Kirby) as well as high sensitivity to disease effects, including the pre-ataxic stage of spinocerebellar ataxia type 3 (SCA3). CerebNet is compatible with FreeSurfer and FastSurfer and can analyze a 3D volume within seconds on a consumer GPU in an end-to-end fashion, thus providing an efficient and validated solution for assessing cerebellum sub-structure volumes. We make CerebNet available as source-code ( https://github.com/Deep-MI/ FastSurfer ).
英文关键词	CerebNet Cerebellum Computational neuroimaging Deep learning
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000884469600009
WOS关键词	OPEN ACCESS SERIES ; OPTIMIZED PATCHMATCH ; LOBULE SEGMENTATION ; MRI DATA ; VOLUMES ; ATAXIA ; ATLAS ; REGISTRATION ; ORGANIZATION ; TEMPLATE
WOS类目	Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393864
推荐引用方式 GB/T 7714	Faber, Jennifer,Kuegler, David,Bahrami, Emad,et al. CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation[J],2022,264.
APA	Faber, Jennifer.,Kuegler, David.,Bahrami, Emad.,Heinz, Lea-Sophie.,Timmann, Dagmar.,...&Reuter, Martin.(2022).CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation.NEUROIMAGE,264.
MLA	Faber, Jennifer,et al."CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation".NEUROIMAGE 264(2022).