An NMR study on internal browning in pears

Internal browning in pears (Pyrus communis L. cv. Blanquilla) has been studied by NMR and MRI in order to develop a non-destructive procedure for on-line disorder identification. For NMR relaxometry, disordered tissue shows higher transverse relaxation rates compared to sound tissue, especially at higher magnetic field strength and for long pulse spacing. Membrane alteration and therefore tissue disintegration, as well as water evaporation, appear to be the main causes of this response. Correlation between relaxation times and diffusion showed that the proton pools in disordered tissue are grouped into a smaller number of populations compared to sound tissue, also highlighting cell decompartmentation in disordered tissue. At a macroscopic level, fast low angle shot MR images, effective transverse relaxation-weighted (TR 11 ms and TE 3.7 ms) and proton density-weighted (TR 7.6 ms and TE 2.5 ms), were acquired for pears at a rate of 54 mm/s. Images have been discriminated for internal breakdown according to histogram characteristics. Up to 94 and 96% of pears, respectively, were correctly classified in the former and the latter type of images. In this study a minimum value of 12% of tissue affected by breakdown was always clearly identifie

Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification

Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer’s disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent, making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance/relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation (“Which change in voxels would change the outcome most?”), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals (“Why does this person have AD?”) with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g., relevance density or relevance gain. Although these metrics produce very individual “fingerprints” of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio

Imaging Informatics and the Human Brain Project: the Role of Structure, Review

(no abstract

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients and healthy controls (n = 147). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of..

Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis

A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipolar disorder, allowing the clinically relevant discrimination between both psychotic diagnostics. To assess the efficacy of sMRI data for diagnostic prediction in psychosis we objectively evaluated the discriminative power of a wide range of commonly used machine learning algorithms (ridge, lasso, elastic net and L0 norm regularized logistic regressions, a support vector classifier, regularized discriminant analysis, random forests and a Gaussian process classifier) on main sMRI features including grey and white matter voxel-based morphometry (VBM), vertex-based cortical thickness and volume, region of interest volumetric measures and wavelet-based morphometry (WBM) maps. All possible combinations of algorithms and data features were considered in pairwise classifications of matched samples of healthy controls (N = 127), patients with schizophrenia (N = 128) and patients with bipolar disorder (N = 128). Results show that the selection of feature type is important, with grey matter VBM (without data reduction) delivering the best diagnostic prediction rates (averaging over classifiers: schizophrenia vs. healthy 75%, bipolar disorder vs. healthy 63% and schizophrenia vs. bipolar disorder 62%) whereas algorithms usually yielded very similar results. Indeed, those grey matter VBM accuracy rates were not even improved by combining all feature types in a single prediction model. Further multi-class classifications considering the three groups simultaneously made evident a lack of predictive power for the bipolar group, probably due to its intermediate anatomical features, located between those observed in healthy controls and those found in patients with schizophrenia. Finally, we provide MRIPredict (https://www.nitrc.org/projects/mripredict/), a free tool for SPM, FSL and R, to easily carry out voxelwise predictions based on VBM images

Gender assessment through three-dimensional analysis of maxillary sinuses by means of Cone Beam Computed Tomography

OBJECTIVE: The availability of a low dose radiation technology such as Cone Beam Computed Tomography (CBCT) in dental practice has increased the number of scans available for forensic purposes. Moreover, specific software allows for three-dimensional (3D) characterization of the maxillary sinuses. This study was performed to determine whether sinus maxillary volumes can be useful to identify gender after validating the use of the Dolphin software as a tool for volumetric estimation of maxillary sinus volumes. PATIENTS AND METHODS: The validation was performed by four different operators measuring the volume of six phantoms, where the real volume was already known. The maxillary sinus volumes of 52 patients (26 males and 26 females) mean age 24.3 were calculated and compared between genders and sagittal skeletal class subdivision. The measurements for patients and phantoms were based on CBCT scans (ILUMA™) processed by Dolphin 3D software. RESULTS: No statistical difference was observed between the real volume and the volume measurements performed by the operators. No statistical difference was found in patient's maxillary sinus volumes between gender. CONCLUSIONS: Based on our results, it is not possible to support the use of maxillary sinuses to discern sexual difference in corpse identification

On the design of a real-time volume rendering engine

An architecture for a Real-Time Volume Rendering Engine (RT-VRE) is given, capable of computing 750 × 750 × 512 samples from a 3D dataset at a rate of 25 images per second. The RT-VRE uses for this purpose 64 dedicated rendering chips, cooperating with 16 RISC-processors. A plane interpolator circuit and a composition circuit, both capable to operate at very high speeds, have been designed for a 1.6 micron VLSI process. Both the interpolator and composition circuit are back from production. They have been tested and both complied with our specifications

Insecure attachment during infancy predicts greater amygdala volumes in early adulthood

Background The quality of the early environment is hypothesized to be an influence on morphological development in key neural areas related to affective responding, but direct evidence to support this possibility is limited. In a 22-year longitudinal study, we examined hippocampal and amygdala volumes in adulthood in relation to early infant attachment status, an important indicator of the quality of the early caregiving environment. Methods Participants (N = 59) were derived from a prospective longitudinal study of the impact of maternal postnatal depression on child development. Infant attachment status (24 Secure; 35 Insecure) was observed at 18 months of age, and MRI assessments were completed at 22 years. Results In line with hypotheses, insecure versus secure infant attachment status was associated with larger amygdala volumes in young adults, an effect that was not accounted for by maternal depression history. We did not find early infant attachment status to predict hippocampal volumes. Conclusions Common variations in the quality of early environment are associated with gross alterations in amygdala morphology in the adult brain. Further research is required to establish the neural changes that underpin the volumetric differences reported here, and any functional implications