Knowing what you know in brain segmentation using Bayesian deep neural networks

In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combining data from more than a hundred different sites, and also evaluated on another completely held-out dataset (n = 418). The network was trained using a novel spike-and-slab dropout-based variational inference approach. We show that, on these datasets, the proposed Bayesian DNN outperforms previously proposed methods, in terms of the similarity between the segmentation predictions and the FreeSurfer labels, and the usefulness of the estimate uncertainty of these predictions. In particular, we demonstrated that the prediction uncertainty of this network at each voxel is a good indicator of whether the network has made an error and that the uncertainty across the whole brain can predict the manual quality control ratings of a scan. The proposed Bayesian DNN method should be applicable to any new network architecture for addressing the segmentation problem.Comment: Submitted to Frontiers in Neuroinformatic

Open and reusable deep learning for pathology with WSInfer and QuPath

The field of digital pathology has seen a proliferation of deep learning models in recent years. Despite substantial progress, it remains rare for other researchers and pathologists to be able to access models published in the literature and apply them to their own images. This is due to difficulties in both sharing and running models. To address these concerns, we introduce WSInfer: a new, open-source software ecosystem designed to make deep learning for pathology more streamlined and accessible. WSInfer comprises three main elements: 1) a Python package and command line tool to efficiently apply patch-based deep learning inference to whole slide images; 2) a QuPath extension that provides an alternative inference engine through user-friendly and interactive software, and 3) a model zoo, which enables pathology models and metadata to be easily shared in a standardized form. Together, these contributions aim to encourage wider reuse, exploration, and interrogation of deep learning models for research purposes, by putting them into the hands of pathologists and eliminating a need for coding experience when accessed through QuPath. The WSInfer source code is hosted on GitHub and documentation is available at https://wsinfer.readthedocs.io

Open and Reusable Deep Learning for Pathology with WSInfer and QuPath

Digital pathology has seen a proliferation of deep learning models in recent years, but many models are not readily reusable. To address this challenge, we developed WSInfer: an open-source software ecosystem designed to streamline the sharing and reuse of deep learning models for digital pathology. The increased access to trained models can augment research on the diagnostic, prognostic, and predictive capabilities of digital pathology

Resting cerebral oxygen metabolism exhibits archetypal network features

Standard magnetic resonance imaging approaches offer high-resolution but indirect measures of neural activity, limiting understanding of the physiological processes associated with imaging findings. Here, we used calibrated functional magnetic resonance imaging during the resting state to recover low-frequency fluctuations of the cerebral metabolic rate of oxygen (CMRO2). We tested whether functional connections derived from these fluctuations exhibited organization properties similar to those established by previous standard functional and anatomical connectivity studies. Seventeen participants underwent 20 min of resting imaging during dual-echo, pseudocontinuous arterial spin labeling, and blood-oxygen-level dependent (BOLD) signal acquisition. Participants also underwent a 10 min normocapnic and hypercapnic procedure. Brain-wide, CMRO2 low-frequency fluctuations were subjected to graph-based and voxel-wise functional connectivity analyses. Results demonstrated that connections derived from resting CMRO2 fluctuations exhibited complex, small-world topological properties (i.e., high integration and segregation, cost efficiency) consistent with those observed in previous studies using functional and anatomical connectivity approaches. Voxel-wise CMRO2 connectivity also exhibited spatial patterns consistent with four targeted resting-state subnetworks: two association (i.e., frontoparietal and default mode) and two perceptual (i.e., auditory and occipital-visual). These are the first findings to support the use of calibration-derived CMRO2 low-frequency fluctuations for detecting brain-wide organizational properties typical of healthy participants. We discuss interpretations, advantages, and challenges in using calibration-derived oxygen metabolism signals for examining the intrinsic organization of the human brain

Evaluating histopathology transfer learning with ChampKit

Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for various tasks, including immune cell detection and microsatellite instability classification. The state-of-the-art for each task often employs base architectures that have been pretrained for image classification on ImageNet. The standard approach to develop classifiers in histopathology tends to focus narrowly on optimizing models for a single task, not considering the aspects of modeling innovations that improve generalization across tasks. Here we present ChampKit (Comprehensive Histopathology Assessment of Model Predictions toolKit): an extensible, fully reproducible benchmarking toolkit that consists of a broad collection of patch-level image classification tasks across different cancers. ChampKit enables a way to systematically document the performance impact of proposed improvements in models and methodology. ChampKit source code and data are freely accessible at https://github.com/kaczmarj/champkit .Comment: Submitted to NeurIPS 2022 Track on Datasets and Benchmarks. Source code available at https://github.com/kaczmarj/champki

Additional file 1: of Rethinking the assessment of risk of bias due to selective reporting: a cross-sectional study

Supplementary tables. (DOCX 57 kb

Fixed and flexible: Dynamic prefrontal activations and working memory capacity relationships vary with memory demand

© 2019 Informa UK Limited, trading as Taylor & Francis Group. Prefrontal cortex (PFC) activation during encoding of memoranda (proactive responses) is associated with better working memory (WM) compared to reactive/retrieval-based activation. This suggests that dynamic PFC activation patterns may be fixed, based upon one’s WM ability, with individuals who have greater WM ability relying more on proactive processes and individuals with lesser WM ability relying more on reactive processes. We newly tested whether this heuristic applied when challenging an individual’s WM capacity. Twenty-two participants (N = 22) underwent functional near-infrared spectroscopy (fNIRS) during a modified Sternberg WM paradigm. We tested whether the relationship between dynamic PFC activation patterns and WM capacity changed, as a function of WM demands (N = 14 after quality control). Here, higher-WM capacity was associated with more proactive PFC patterns, but only when WM capacity was overloaded. Lower-WM capacity was associated with these same patterns, but only when WM demand was low. Findings are inconsistent with a purely fixed view of dynamic PFC activation patterns and suggest higher- and lower-WM-capacity individuals flexibly engage PFC processes in a fundamentally different manner, dependent upon current WM demands.National Institutes of Health (Grant F32MH114525

Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. 0.13.1

0.13.1 (May 20, 2017) FIX: Make release compatible with conda-forge build process (https://github.com/nipy/nipype/pull/2017) ENH: Update some minimum versions in compliance with Debian Jessie (https://github.com/nipy/nipype/pull/2017) ENH: Circle builds use cached docker layers (https://github.com/nipy/nipype/pull/2017) ENH: Base docker to use FS6 and ANTS 2.2.0 (https://github.com/nipy/nipype/pull/2024) FIX: Mailmap and contributor acknowledgment (https://github.com/nipy/nipype/pull/2017) FIX: Preserve node properties in sub nodes of MapNode (https://github.com/nipy/nipype/pull/2019) FIX: Fix interfaces 3DUnifize, ICA_AROMA, BinaryMaths, RegAverage, BBRegister, AffineInitializer (https://github.com/nipy/nipype/pull/2025, https://github.com/nipy/nipype/pull/2027, https://github.com/nipy/nipype/pull/2036, https://github.com/nipy/nipype/pull/2037, https://github.com/nipy/nipype/pull/2031, https://github.com/nipy/nipype/pull/2010) ENH: Add Anisotropic Power interface (https://github.com/nipy/nipype/pull/2039) FIX: Bayesian estimation in SPM (https://github.com/nipy/nipype/pull/2030) 0.13.0 (May 11, 2017) ENH: Multi-stage recon-all directives (https://github.com/nipy/nipype/pull/1991) FIX: FEAT "folder does not exist" error (https://github.com/nipy/nipype/pull/2000) ENH: Niftyfit interfaces (https://github.com/nipy/nipype/pull/1910) FIX: Define ANTSPATH for BrainExtraction automatically (https://github.com/nipy/nipype/pull/1986) ENH: New trait for imaging files (https://github.com/nipy/nipype/pull/1949) ENH: Niftyseg interfaces (https://github.com/nipy/nipype/pull/1911) ENH: Niftyreg interfaces (https://github.com/nipy/nipype/pull/1913) MRG: Allow more support for CLI (https://github.com/nipy/nipype/pull/1908) ENH: 3dQwarpPlusMinus interface (https://github.com/nipy/nipype/pull/1974) FIX: PY3.6 support (https://github.com/nipy/nipype/pull/1977) FIX: PY3 and stream fixes for MRTrix2TrackVis (https://github.com/nipy/nipype/pull/1804) ENH: More mask options for CompCor interfaces (https://github.com/nipy/nipype/pull/1968 + https://github.com/nipy/nipype/pull/1992) ENH: Additional TOPUP outputs (https://github.com/nipy/nipype/pull/1976) ENH: Additional Eddy flags (https://github.com/nipy/nipype/pull/1967) ENH: ReconAll handlers for less common cases (https://github.com/nipy/nipype/pull/1966) ENH: FreeSurferSource now finds graymid/midthickness surfs (https://github.com/nipy/nipype/pull/1972) ENH: Additional fslmaths dimensional reduction operations (https://github.com/nipy/nipype/pull/1956) ENH: More options for RobustFOV interface (https://github.com/nipy/nipype/pull/1923) ENH: Add MRIsCombine to FreeSurfer utils (https://github.com/nipy/nipype/pull/1948) FIX: Level1Design EV parameter substitution (https://github.com/nipy/nipype/pull/1953) FIX: Dcm2niix outputs can be uncompressed (https://github.com/nipy/nipype/pull/1951) FIX: Ensure build fails in Circle when tests fail (https://github.com/nipy/nipype/pull/1981) ENH: Add interface to antsAffineInitializer (https://github.com/nipy/nipype/pull/1980) ENH: AFNI motion parameter support for FrameWiseDisplacement (https://github.com/nipy/nipype/pull/1840) ENH: Add ANTs KellyKapowski interface (https://github.com/nipy/nipype/pull/1845) FIX: AFNI interface bug setting OMP_NUM_THREADS to 1 (https://github.com/nipy/nipype/pull/1728) FIX: Select Eddy run command at runtime (https://github.com/nipy/nipype/pull/1871) FIX: Increase FLIRT's flexibility with apply_xfm (https://github.com/nipy/nipype/pull/1875) DOC: Update FSL preprocess docstrings (https://github.com/nipy/nipype/pull/1881) ENH: Support GIFTI outputs in SampleToSurface (https://github.com/nipy/nipype/pull/1886) FIX: Configparser differences between PY2 and PY3 (https://github.com/nipy/nipype/pull/1890) ENH: Add mris_expand interface (https://github.com/nipy/nipype/pull/1893) FIX: Split over-eager globs in FreeSurferSource (https://github.com/nipy/nipype/pull/1894) FIX: Store undefined by default so that xor checks don't trip (https://github.com/nipy/nipype/pull/1903) FIX: Gantt chart generator PY3 compatibility (https://github.com/nipy/nipype/pull/1907) FIX: Add DOF and --fsl-dof options to BBRegister (https://github.com/nipy/nipype/pull/1917) ENH: Auto-derive input_names in Function (https://github.com/nipy/nipype/pull/1918) FIX: Minor fixes for NonSteadyStateDetector (https://github.com/nipy/nipype/pull/1926) DOC: Add duecredit references for AFNI and FSL (https://github.com/nipy/nipype/pull/1930) ENH: Added zenodo (https://zenodo.org/) file (https://github.com/nipy/nipype/pull/1924) ENH: Disable symlinks on CIFS filesystems (https://github.com/nipy/nipype/pull/1941) ENH: Sphinx extension to plot workflows (https://github.com/nipy/nipype/pull/1896) ENH: Added non-steady state detector for EPI data (https://github.com/nipy/nipype/pull/1839) ENH: Enable new BBRegister init options for FSv6+ (https://github.com/nipy/nipype/pull/1811) REF: Splits nipype.interfaces.utility into base, csv, and wrappers (https://github.com/nipy/nipype/pull/1828) FIX: Makespec now runs with nipype in current directory (https://github.com/nipy/nipype/pull/1813) FIX: Flexible nifti opening with mmap if Numpy < 1.12.0 (https://github.com/nipy/nipype/pull/1796 + https://github.com/nipy/nipype/pull/1831) ENH: DVARS includes intensity normalization feature - turned on by default (https://github.com/nipy/nipype/pull/1827) FIX: DVARS is correctly using sum of squares instead of standard deviation (https://github.com/nipy/nipype/pull/1827) ENH: Refactoring of nipype.interfaces.utility (https://github.com/nipy/nipype/pull/1828) FIX: CircleCI were failing silently. Some fixes to tests (https://github.com/nipy/nipype/pull/1833) FIX: Issues in Docker image permissions, and docker documentation (https://github.com/nipy/nipype/pull/1825) ENH: Revised all Dockerfiles and automated deployment to Docker Hub from CircleCI (https://github.com/nipy/nipype/pull/1815) ENH: Update ReconAll interface for FreeSurfer v6.0.0 (https://github.com/nipy/nipype/pull/1790) FIX: Cast DVARS float outputs to avoid memmap error (https://github.com/nipy/nipype/pull/1777) FIX: FSL FNIRT intensity mapping files (https://github.com/nipy/nipype/pull/1799) ENH: Additional outputs generated by FSL EDDY (https://github.com/nipy/nipype/pull/1793) TST: Parallelize CircleCI build across 4 containers (https://github.com/nipy/nipype/pull/1769) 0.13.0-rc1 (January 4, 2017) FIX: Compatibility with traits 4.6 (https://github.com/nipy/nipype/pull/1770) FIX: Multiproc deadlock (https://github.com/nipy/nipype/pull/1756) TST: Replace nose and unittest with pytest (https://github.com/nipy/nipype/pull/1722, https://github.com/nipy/nipype/pull/1751) FIX: Semaphore capture using MultiProc plugin (https://github.com/nipy/nipype/pull/1689) REF: Refactor AFNI interfaces (https://github.com/nipy/nipype/pull/1678, https://github.com/nipy/nipype/pull/1680) ENH: Move nipype commands to group command using click (https://github.com/nipy/nipype/pull/1608) FIX: AFNI Retroicor interface fixes (https://github.com/nipy/nipype/pull/1669) FIX: Minor errors after migration to setuptools (https://github.com/nipy/nipype/pull/1671) ENH: Add AFNI 3dNote interface (https://github.com/nipy/nipype/pull/1637) ENH: Abandon distutils, only use setuptools (https://github.com/nipy/nipype/pull/1627) FIX: Minor bugfixes related to unicode literals (https://github.com/nipy/nipype/pull/1656) TST: Automatic retries in travis (https://github.com/nipy/nipype/pull/1659/files) ENH: Add signal extraction interface (https://github.com/nipy/nipype/pull/1647) ENH: Add a DVARS calculation interface (https://github.com/nipy/nipype/pull/1606) ENH: New interface to b0calc of FSL-POSSUM (https://github.com/nipy/nipype/pull/1399) ENH: Add CompCor (https://github.com/nipy/nipype/pull/1599) ENH: Add duecredit entries (https://github.com/nipy/nipype/pull/1466) FIX: Python 3 compatibility fixes (https://github.com/nipy/nipype/pull/1572) REF: Improved PEP8 compliance for fsl interfaces (https://github.com/nipy/nipype/pull/1597) REF: Improved PEP8 compliance for spm interfaces (https://github.com/nipy/nipype/pull/1593) TST: Replaced coveralls with codecov (https://github.com/nipy/nipype/pull/1609) ENH: More BrainSuite interfaces (https://github.com/nipy/nipype/pull/1554) ENH: Convenient load/save of interface inputs (https://github.com/nipy/nipype/pull/1591) ENH: Add a Framewise Displacement calculation interface (https://github.com/nipy/nipype/pull/1604) FIX: Use builtins open and unicode literals for py3 compatibility (https://github.com/nipy/nipype/pull/1572) TST: reduce the size of docker images & use tags for images (https://github.com/nipy/nipype/pull/1564) ENH: Implement missing inputs/outputs in FSL AvScale (https://github.com/nipy/nipype/pull/1563) FIX: Fix symlink test in copyfile (https://github.com/nipy/nipype/pull/1570, https://github.com/nipy/nipype/pull/1586) ENH: Added support for custom job submission check in SLURM (https://github.com/nipy/nipype/pull/1582) ENH: Added ANTs interface CreateJacobianDeterminantImage; replaces deprecated JacobianDeterminant (https://github.com/nipy/nipype/pull/1654