Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation

New insights on speech signal modeling in a Bayesian framework approach

Speech signal processing is an old research topic within the communication theory community. The continously increasing telephony market brought special attention to the discipline during the 80’s and 90’s, specially in speech coding and speech enhancement, where the most significant contributions were made. More recently, due to the appearance of novel signal processing techniques, the standard methods are being questioned. Sparse representation of signals and compessed sensing made significant contributions to the discipline, through a better representation of signals and more efficient processing techniques. In this thesis, standard speech modeling techniques are revisited. Firstly, a representation of the speech signal through the line spectral frequencies (LSF) is presented, with a extended stability analysis. Moreover, a new Bayesian framework to time-varying linear prediction (TVLP) is shown, with the analysis of different methods. Finally, a theoretical basis for speech denoising is presented and analyzed. At the end of the thesis, the reader will have a broader view of the speech signal processing discipline with new insights that can improve the standard methodology

BAYESIAN LEARNING FOR TIME-VARYING LINEAR PREDICTION OF SPEECH

We develop Bayesian learning algorithms for estimation of time-varying linear prediction (TVLP) coefficients of speech. Estimation of TVLP coefficients is a naturally underdetermined problem. We consider sparsity and subspace based approaches for dealing with the corresponding underdetermined system. Bayesian learning algorithms are developed to achieve better estimation performance. Expectation-maximization (EM) framework is employed to develop the Bayesian learning algorithms where we use a combined prior to model a driving noise (glottal signal) that has both sparse and dense statistical properties. The efficiency of the Bayesian learning algorithms is shown for synthetic signals using spectral distortion measure and formant tracking of real speech signals

Registered histology, MRI, and manual annotations of over 300 brain regions in 5 human hemispheres (data from ERC Starting Grant 677697 "BUNGEE-TOOLS")

Summary: This repository includes data related to the ERC Starting Grant project 677697: "Building Next-Generation Computational Tools for High Resolution Neuroimaging Studies" (BUNGEE-TOOLS). It includes: (a) Dense histological sections from five human hemispheres with manual delineations of >300 brain regions; (b) Corresponding ex vivo MRI scans; (c) Dissection photographs; (d) A spatially aligned version of the dataset; (e) A probabilistic atlas built from the hemispheres; and (f) Code to apply the atlas to automated segmentation of in vivo MRI scans. More detailed description on what this dataset includes: Data files and Python code for Bayesian segmentation of human brain MRI based on a next-generation, high-resolution histological atlas: "Next-Generation histological atlas for high-resolution segmentation of human brain MRI" A Casamitjana et al., in preparation.  This repository contains a set of zip files, each corresponding to one directory. Once decompressed, each directory has a readme.txt file explaining its contents.   The list of zip files / compressed directories is:   - 3dAtlas.zip: nifti files with summary imaging volumes of the probabilistic atlas.   - BlockFacePhotoBlocks.zip: nifti files with the blackface photographs acquired during   tissue sectioning, reconstructed into 3D volumes (in RGB).    - Histology.zip: jpg files with the LFB and H&E stained sections.   - HistologySegmentations.zip: 2D nifti files with the segmentations of the histological sections.   - MRI.zip: ex vivo T2-weighted MRI scans and corresponding FreeSurfer processing files   - SegmentationCode.zip: contains the the Python code and data files that we used to segment   brain MRI scans and obtain the results presented in the article (for reproducibility purposes).   Note that it requires an installation of FreeSurfer. Also, note that the code is also maintained    in FreeSurfer (but may not produce exactly the same results):   https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation   - WholeHemispherePhotos.zip: photographs of the specimens prior to dissection   - WholeSlicePhotos.zip: photographs of the tissue slabs prior to blocking.    We also note that the registered images for the five cases can be found in GitHub:  https://github.com/UCL/BrainAtlas-P41-16  https://github.com/UCL/BrainAtlas-P57-16  https://github.com/UCL/BrainAtlas-P58-16  https://github.com/UCL/BrainAtlas-P85-18  https://github.com/UCL/BrainAtlas-EX9-19   These registered images can be interactively explored with the following web interface:  https://github-pages.ucl.ac.uk/BrainAtlas/#/atlas</p

Benchmark on automatic six-month-old infant brain segmentation algorithms:The iSeg-2017 Challenge

\u3cp\u3eAccurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.\u3c/p\u3

Benchmark on automatic six-month-old infant brain segmentation algorithms:The iSeg-2017 Challenge

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.</p

Benchmark on automatic six-month-old infant brain segmentation algorithms: The iSeg-2017 Challenge

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community