Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands or tens of thousands of individuals. In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer’s anatomical segmentation including surface reconstruction and cortical parcellation. To this end, we introduce an advanced deep learning architecture capable of whole-brain segmentation into 95 classes. The network architecture incorporates local and global competition via competitive dense blocks and competitive skip pathways, as well as multi-slice information aggregation that specifically tailor network performance towards accurate segmentation of both cortical and subcortical structures. Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface. This approach provides a full FreeSurfer alternative for volumetric analysis (in under 1 min) and surface-based thickness analysis (within only around 1 h runtime). For sustainability of this approach we perform extensive validation: we assert high segmentation accuracy on several unseen datasets, measure generalizability and demonstrate increased test-retest reliability, and high sensitivity to group differences in dementia.

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. Method: FatSegNet is composed of three stages: (i) Consistent localization of the abdominal region using two 2D-Competitive Dense Fully Convolutional Networks (CDFNet), (ii) Segmentation of adipose tissue on three views by independent CDFNets, and (iii) 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 Studya 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). 2 SUBMITTED TO MAGNETIC RESONANCE IN MEDICINE Conclusion: 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 ∼ 1 min, providing an efficient and validated pipeline for abdominal adipose tissue analysis in the Rhineland Study. K E Y W O R D S Subcutaneous adipose tissue, Visceral adipose tissue, Dixon MRI, Neural networks, Deep learning, Semantic segmentation 1 | INTRODUCTION The excess of body fat depots is an increasing major public health issue worldwide and an important risk factor for the development of metabolic disorders and reduced quality of life [1, 2]. While the body mass index (BMI) is a widely used indicator of adipose tissue accumulation in the body, it does not provide information on fat distribution [3] -neither with respect to different fat tissue types nor with respect to deposit location. Different compartments of adipose tissue are associated with different physiopathological effects [4, 5]. Abdominal adipose tissue (AAT), composed of subcutaneous and visceral adipose tissue (SAT and VAT), has long been associated with an increased risk of chronic cardiovascular diseases, glucose impairment, and dyslipidemia [6, 7]. Recently, several studies have indicated a stronger relation between the accumulation of VAT with an adverse metabolic and inflammatory profile compared to SAT [8, 9]. Therefore, an accurate and independent measurement of VAT and SAT volumes (VAT-V and SAT-V) is of significant clinical and research interest. Currently, the gold standard for measuring VAT-V and SAT-V is the manual segmentation of abdominal fat images from Dixon magnetic resonance (MR) scans -a very expensive and time-consu...

Undersampling the k -space data is widely adopted for acceleration of Magnetic Resonance Imaging (MRI). Current deep learning based approaches for supervised learning of MRI image reconstruction employ real-valued operations and representations by treating complex valued k-space/spatial-space as real values. In this paper, we propose complex dense fully convolutional neural network (CDFNet) for learning to de-alias the reconstruction artifacts within undersampled MRI images. We fashioned a densely-connected fully convolutional block tailored for complex-valued inputs by introducing dedicated layers such as complex convolution, batch normalization, non-linearities etc. CDFNet leverages the inherently complex-valued nature of input k -space and learns richer representations. We demonstrate improved perceptual quality and recovery of anatomical structures through CDFNet in contrast to its realvalued counterparts.

Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies. With FastSurfer [1] we propose a fast deep-learning based alternative for the automated processing of structural human MRI brain scans, including surface reconstruction and cortical parcellation. FastSurfer consists of an advanced deep learning architecture (FastSurferCNN) used to segment a whole brain MRI into 95 classes in under 1 min, and a surface pipeline building upon this high-quality brain segmentation. FastSurferCNN incorporates local and global competition via competitive dense blocks and competitive skip pathways, as well as multi-slice information aggregation that specifically tailor network performance towards accurate recognition of both cortical and sub-cortical structures. We demonstrate the superior performance of FastSurferCNN across five different datasets where it consistently outperforms existing deep learning approaches in terms of accuracy by a margin. Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface. Precisely, we use the eigenfunctions of the Laplace-Beltrami operator to parametrize the surface smoothly and quickly generate the final spherical map by scaling the 3D spectral embedding vector to unit length. For sustainability of the pipeline we perform extensive validation of FastSurfer: we measure generalizability to different scanners, disease states, as well as an unseen acquisition sequence, demonstrate increased test-retest reliability, and increased sensitivity to disease effects relative to traditional FreeSurfer. In total, we provide a reliable full FreeSurfer alternative for volumetric analysis (within 1 minute) and surface-based thickness analysis (within only around 1h + optionally 30 min for group registration). References1. Henschel L, Conjeti S, Estrada S, et al. FastSurfer -a fast and accurate deep learning based neuroimaging pipeline. CoRR. 2019;abs/1910.03866.