CerebNet Segmentation Models for Cerebellar Sug-Segmentation

DOI

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 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).

Three files for trained models of the three different slicing directions.

Identifier
DOI https://doi.org/10.34730/d440e937e9214b9b869213f113e97bdd
Source https://b2share.fz-juelich.de/records/d440e937e9214b9b869213f113e97bdd
Related Identifier https://doi.org/10.1016/j.neuroimage.2022.119703
Metadata Access https://b2share.fz-juelich.de/api/oai2d?verb=GetRecord&metadataPrefix=eudatcore&identifier=oai:b2share.fz-juelich.de:b2rec/d440e937e9214b9b869213f113e97bdd
Provenance
Creator Kügler, David; Reuter, Martin; Faber, Jenny; Bahrami-Rad, Emad
Publisher EUDAT B2SHARE
Publication Year 2023
Rights Creative Commons Attribution (CC-BY); info:eu-repo/semantics/openAccess
OpenAccess true
Contact david.kuegler(at)dzne.de
Representation
Resource Type Model
Format pkl
Size 65.1 MB; 3 files
Version 1.0
Discipline 4.1.17.1.2.1 → Machine learning → Artificial neural network; 4.1.18.9 → Mathematics → Computational neuroscience; 3.1.27 → Biology → Neuroscience