Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research

DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its high modularity and easy-to-use examples allow for a low-threshold access to state-of-the-art implementations for typical medical imaging problems. A comparison of DLTK's reference implementations of popular network architectures for image segmentation demonstrates new top performance on the publicly available challenge data "Multi-Atlas Labeling Beyond the Cranial Vault". The average test Dice similarity coefficient of $81.5$ exceeds the previously best performing CNN ($75.7$) and the accuracy of the challenge winning method ($79.0$)

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the various proposed networks perform differently, with behaviour largely influenced by architectural choices and training settings. This paper explores Ensembles of Multiple Models and Architectures (EMMA) for robust performance through aggregation of predictions from a wide range of methods. The approach reduces the influence of the meta-parameters of individual models and the risk of overfitting the configuration to a particular database. EMMA can be seen as an unbiased, generic deep learning model which is shown to yield excellent performance, winning the first position in the BRATS 2017 competition among 50+ participating teams.Comment: The method won the 1st-place in the Brain Tumour Segmentation (BRATS) 2017 competition (segmentation task

Carboxyl-Terminal Cleavage of Apolipoprotein A-I by Human Mast Cell Chymase Impairs Its Anti-Inflammatory Properties

Objective Apolipoprotein A-I (apoA-I) has been shown to possess several atheroprotective functions, including inhibition of inflammation. Protease-secreting activated mast cells reside in human atherosclerotic lesions. Here we investigated the effects of the neutral proteases released by activated mast cells on the anti-inflammatory properties of apoA-I. Approach and Results Activation of human mast cells triggered the release of granule-associated proteases chymase, tryptase, cathepsin G, carboxypeptidase A, and granzyme B. Among them, chymase cleaved apoA-I with the greatest efficiency and generated C-terminally truncated apoA-I, which failed to bind with high affinity to human coronary artery endothelial cells. In tumor necrosis factor--activated human coronary artery endothelial cells, the chymase-cleaved apoA-I was unable to suppress nuclear factor-B-dependent upregulation of vascular cell adhesion molecule-1 (VCAM-1) and to block THP-1 cells from adhering to and transmigrating across the human coronary artery endothelial cells. Chymase-cleaved apoA-I also had an impaired ability to downregulate the expression of tumor necrosis factor-, interleukin-1, interleukin-6, and interleukin-8 in lipopolysaccharide-activated GM-CSF (granulocyte-macrophage colony-stimulating factor)- and M-CSF (macrophage colony-stimulating factor)-differentiated human macrophage foam cells and to inhibit reactive oxygen species formation in PMA (phorbol 12-myristate 13-acetate)-activated human neutrophils. Importantly, chymase-cleaved apoA-I showed reduced ability to inhibit lipopolysaccharide-induced inflammation in vivo in mice. Treatment with chymase blocked the ability of the apoA-I mimetic peptide L-4F, but not of the protease-resistant D-4F, to inhibit proinflammatory gene expression in activated human coronary artery endothelial cells and macrophage foam cells and to prevent reactive oxygen species formation in activated neutrophils. Conclusions The findings identify C-terminal cleavage of apoA-I by human mast cell chymase as a novel mechanism leading to loss of its anti-inflammatory functions. When targeting inflamed protease-rich atherosclerotic lesions with apoA-I, infusions of protease-resistant apoA-I might be the appropriate approach.Peer reviewe

Prone to Supine CT Colonography Registration Using a Landmark and Intensity Composite Method

Matching corresponding location between prone and supine acquisitions for CT colonography (CTC) is essential to verify the existence of a polyp, which can be a difficult task due to the considerable deformations that will often occur to the colon during repositioning of the patient. This can induce error and increase interpretation time. We propose a novel method to automatically establish correspondence between the two acquisitions. A first step segments a set of haustral folds in each view and determines correspondence via a labelling process using a Markov Random Field (MRF) model. We show how the landmark correspondences can be used to non-rigidly transform a 2D source image derived from a conformal mapping process on the 3D endoluminal surface mesh to achieve full surface correspondence between prone and supine views. This can be used to initialise an intensity-based non-rigid B-spline registration method which further increases the accuracy. We demonstrate a statistically significant improvement over the intensity based non-rigid B-spline registration by using the composite method

Evaluation of Six Registration Methods for the Human Abdomen on Clinically Acquired CT

Objective: This work evaluates current 3-D image registration tools on clinically acquired abdominal computed tomography (CT) scans. Methods: Thirteen abdominal organs were manually labeled on a set of 100 CT images, and the 100 labeled images (i.e., atlases) were pairwise registered based on intensity information with six registration tools (FSL, ANTS-CC, ANTS-QUICK-MI, IRTK, NIFTYREG, and DEEDS). The Dice similarity coefficient (DSC), mean surface distance, and Hausdorff distance were calculated on the registered organs individually. Permutation tests and indifference-zone ranking were performed to examine the statistical and practical significance, respectively. Results: The results suggest that DEEDS yielded the best registration performance. However, due to the overall low DSC values, and substantial portion of low-performing outliers, great care must be taken when image registration is used for local interpretation of abdominal CT. Conclusion: There is substantial room for improvement in image registration for abdominal CT. Significance: All data and source code are available so that innovations in registration can be directly compared with the current generation of tools without excessive duplication of effort

A unique protease-sensitive high density lipoprotein particle containing the apolipoprotein A-I(Milano) dimer effectively promotes ATP-binding Cassette A1-mediated cell cholesterol efflux

Carriers of the apolipoprotein A-I-Milano (A-I-M) variant present with severe reductions of plasma HDL levels, not associated with premature coronary heart disease (CHD). Sera from 14 A-I-M. carriers and matched controls were compared for their ability to promote ABCA1-driven cholesterol efflux from J774 macrophages and human fibroblasts. When both cell types are stimulated to express ABCA1, the efflux of cholesterol through this pathway is greater with A-I-M than control sera (3.4 +/- 1.0% versus 2.3 +/- 1.0% in macrophages; 5.2 +/- 2.4% versus 1.9 +/- 0.1% in fibroblasts). A-I-M and control sera are instead equally effective in removing cholesterol from unstimulated cells and from fibroblasts not expressing ABCA1. The A-I-M sera contain normal amounts of apoA-I-containing pre beta-HDL and varying concentrations of a unique small HDL particle containing a single molecule of the A-I-M, dimer; chymase treatment of serum degrades both particles and abolishes ABCA1-mediated cholesterol efflux. The serum content of chymase-sensitive HDL correlates strongly and significantly with ABCA1-mediated cholesterol efflux (r = 0.542, p = 0.004). The enhanced capacity of A-I-M serum for ABCA1 cholesterol efflux is thus explained by the combined occurrence in serum of normal amounts of apoA-I-containing pre beta-HDL, together with a unique protease-sensitive, small HDL particle containing the A-I-M dimer, both effective in removing cell cholesterol via ABCA1

Voxel-wise comparisons of cellular microstructure and diffusion-MRI in mouse hippocampus using 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND)

A key challenge in medical imaging is determining a precise correspondence between image properties and tissue microstructure. This comparison is hindered by disparate scales and resolutions between medical imaging and histology. We present a new technique, 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND), for registering medical images with 3D histology to overcome these limitations. Ex vivo 120 × 120 × 200 μm resolution diffusion-MRI (dMRI) data was acquired at 7 T from adult C57Bl/6 mouse hippocampus. Tissue was then optically cleared using CLARITY and stained with cellular markers and confocal microscopy used to produce high-resolution images of the 3D-tissue microstructure. For each sample, a dense array of hippocampal landmarks was used to drive registration between upsampled dMRI data and the corresponding confocal images. The cell population in each MRI voxel was determined within hippocampal subregions and compared to MRI-derived metrics. 3D-BOND provided robust voxel-wise, cellular correlates of dMRI data. CA1 pyramidal and dentate gyrus granular layers had significantly different mean diffusivity (p > 0.001), which was related to microstructural features. Overall, mean and radial diffusivity correlated with cell and axon density and fractional anisotropy with astrocyte density, while apparent fibre density correlated negatively with axon density. Astrocytes, axons and blood vessels correlated to tensor orientation

Apraxia and motor dysfunction in corticobasal syndrome

Background: Corticobasal syndrome (CBS) is characterized by multifaceted motor system dysfunction and cognitive disturbance; distinctive clinical features include limb apraxia and visuospatial dysfunction. Transcranial magnetic stimulation (TMS) has been used to study motor system dysfunction in CBS, but the relationship of TMS parameters to clinical features has not been studied. The present study explored several hypotheses; firstly, that limb apraxia may be partly due to visuospatial impairment in CBS. Secondly, that motor system dysfunction can be demonstrated in CBS, using threshold-tracking TMS, and is linked to limb apraxia. Finally, that atrophy of the primary motor cortex, studied using voxel-based morphometry analysis (VBM), is associated with motor system dysfunction and limb apraxia in CBS.   Methods: Imitation of meaningful and meaningless hand gestures was graded to assess limb apraxia, while cognitive performance was assessed using the Addenbrooke's Cognitive Examination - Revised (ACE-R), with particular emphasis placed on the visuospatial subtask. Patients underwent TMS, to assess cortical function, and VBM.   Results: In total, 17 patients with CBS (7 male, 10 female; mean age 64.4+/2 6.6 years) were studied and compared to 17 matched control subjects. Of the CBS patients, 23.5% had a relatively inexcitable motor cortex, with evidence of cortical dysfunction in the remaining 76.5% patients. Reduced resting motor threshold, and visuospatial performance, correlated with limb apraxia. Patients with a resting motor threshold <50% performed significantly worse on the visuospatial sub-task of the ACE-R than other CBS patients. Cortical function correlated with atrophy of the primary and pre-motor cortices, and the thalamus, while apraxia correlated with atrophy of the pre-motor and parietal cortices.   Conclusions: Cortical dysfunction appears to underlie the core clinical features of CBS, and is associated with atrophy of the primary motor and pre-motor cortices, as well as the thalamus, while apraxia correlates with pre-motor and parietal atrophy

Organ-focused mutual information for nonrigid multimodal registration of liver CT and Gd–EOB–DTPA-enhanced MRI

Accurate detection of liver lesions is of great importance in hepatic surgery planning. Recent studies have shown that the detection rate of liver lesions is significantly higher in gadoxetic acid-enhanced magnetic resonance imaging (Gd–EOB–DTPA-enhanced MRI) than in contrast-enhanced portal-phase computed tomography (CT); however, the latter remains essential because of its high specificity, good performance in estimating liver volumes and better vessel visibility. To characterize liver lesions using both the above image modalities, we propose a multimodal nonrigid registration framework using organ-focused mutual information (OF-MI). This proposal tries to improve mutual information (MI) based registration by adding spatial information, benefiting from the availability of expert liver segmentation in clinical protocols. The incorporation of an additional information channel containing liver segmentation information was studied. A dataset of real clinical images and simulated images was used in the validation process. A Gd–EOB–DTPA-enhanced MRI simulation framework is presented. To evaluate results, warping index errors were calculated for the simulated data, and landmark-based and surface-based errors were calculated for the real data. An improvement of the registration accuracy for OF-MI as compared with MI was found for both simulated and real datasets. Statistical significance of the difference was tested and confirmed in the simulated dataset (p < 0.01)