DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation

Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturation and aging. Despite the availability of open-source brain segmentation software, widespread clinical adoption of volumetric analysis has been hindered due to processing times and reliance on manual corrections. Here, we extend the use of deep learning models from proof-of-concept, as previously reported, to present a comprehensive segmentation of cortical and deep gray matter brain structures matching the standard regions of aseg+aparc included in the commonly used open-source tool, Freesurfer. The work presented here provides a real-life, rapid deep learning-based brain segmentation tool to enable clinical translation as well as research application of quantitative brain segmentation. The advantages of the presented tool include short (~1 minute) processing time and improved segmentation quality. This is the first study to perform quick and accurate segmentation of 102 brain regions based on the surface-based protocol (DMK protocol), widely used by experts in the field. This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model. We show the superior performance of our deep-learning-based models over the traditional segmentation tool, Freesurfer. We refer to the proposed deep learning-based tool as DARTS (DenseUnet-based Automatic Rapid Tool for brain Segmentation). Our tool and trained models are available at this https URL

[1]  Vladimir Fonov,et al.  Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge , 2015, NeuroImage.

[2]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[3]  I. Melle,et al.  Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium , 2016, Molecular Psychiatry.

[4]  Arthur W. Toga,et al.  Enhancement of T2 and proton density MR images using registration for signal averaging , 1996, NeuroImage.

[5]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[6]  Toan Duc Bui,et al.  3D Densely Convolutional Networks for Volumetric Segmentation , 2017, ArXiv.

[7]  John G. Csernansky,et al.  Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults , 2007, Journal of Cognitive Neuroscience.

[8]  H. Wiendl,et al.  Clinical Relevance of Brain Volume Measures in Multiple Sclerosis , 2014, CNS Drugs.

[9]  R. Peters,et al.  Ageing and the brain , 2006, Postgraduate Medical Journal.

[10]  Arno Klein,et al.  101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol , 2012, Front. Neurosci..

[11]  R. Mayeux,et al.  Hippocampal and entorhinal atrophy in mild cognitive impairment , 2007, Neurology.

[12]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[13]  Xavier Lladó,et al.  Quantitative Analysis of Patch-Based Fully Convolutional Neural Networks for Tissue Segmentation on Brain Magnetic Resonance Imaging , 2018, IEEE Access.

[14]  Ian D. Reid,et al.  RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Joylee Wu,et al.  The National Alzheimer's Coordinating Center (NACC) Database: The Uniform Data Set , 2007, Alzheimer disease and associated disorders.

[16]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[17]  Pierrick Coupé,et al.  Lifespan Changes of the Human Brain In Alzheimer’s Disease , 2019, Scientific Reports.

[18]  Sotirios A. Tsaftaris,et al.  Medical Image Computing and Computer Assisted Intervention , 2017 .

[19]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[20]  Á. Ruibal,et al.  Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models , 2019, NeuroImage: Clinical.

[21]  Shunxing Bao,et al.  3D whole brain segmentation using spatially localized atlas network tiles , 2019, NeuroImage.

[22]  B. Damasceno.  Neuroimaging in normal pressure hydrocephalus , 2015, Dementia & neuropsychologia.

[23]  K. McGraw,et al.  Forming inferences about some intraclass correlation coefficients. , 1996 .

[24]  D. Pelletier,et al.  Contribution of normal aging to brain atrophy in MS , 2019, Neurology: Neuroimmunology & Neuroinflammation.

[25]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[26]  Yoshua Bengio,et al.  The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[27]  P. Bosco,et al.  Brain atrophy in Alzheimer’s Disease and aging , 2016, Ageing Research Reviews.

[28]  Shuiwang Ji,et al.  Deep convolutional neural networks for multi-modality isointense infant brain image segmentation , 2015, NeuroImage.

[29]  Richard L. Doty,et al.  Combining Early Markers Strongly Predicts Conversion from Mild Cognitive Impairment to Alzheimer's Disease , 2008, Biological Psychiatry.

[30]  Paul A. Yushkevich,et al.  Multi-atlas segmentation with joint label fusion and corrective learning—an open source implementation , 2013, Front. Neuroinform..

[31]  Jose Dolz,et al.  3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study , 2016, NeuroImage.

[32]  Bruce Fischl,et al.  Within-subject template estimation for unbiased longitudinal image analysis , 2012, NeuroImage.

[33]  Abraham Z. Snyder,et al.  Human Connectome Project informatics: Quality control, database services, and data visualization , 2013, NeuroImage.

[34]  D. Cicchetti.  Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology. , 1994 .

[35]  Hao Chen,et al.  VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation , 2016, ArXiv.

[36]  Sébastien Ourselin,et al.  Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations , 2017, DLMIA/ML-CDS@MICCAI.

[37]  Rob Fergus,et al.  Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[38]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  John C. Gore,et al.  Integrating Functional and Diffusion Magnetic Resonance Imaging for Analysis of Structure-Function Relationship in the Human Language Network , 2009, PloS one.

[40]  Christian Wachinger,et al.  DeepNAT: Deep convolutional neural network for segmenting neuroanatomy , 2017, NeuroImage.

[41]  George Papandreou,et al.  Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.

[42]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[43]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[44]  Min Chen,et al.  Multi-parametric neuroimaging reproducibility: A 3-T resource study , 2011, NeuroImage.

[45]  Elena Marchiori,et al.  Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities , 2016, Scientific Reports.

[46]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[47]  Seyed-Ahmad Ahmadi,et al.  Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound , 2016, Comput. Vis. Image Underst..

[48]  Eduard T. Klapwijk,et al.  Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data , 2019, NeuroImage.