Crop and Couple: Cardiac Image Segmentation Using Interlinked Specialist Networks

Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation. We propose a novel strategy that performs segmentation using specialist networks that focus on a single anatomy (left ventricle, right ventricle, or myocardium). Given an input long-axis cardiac MR image, our method performs a ternary segmentation in the first stage to identify these anatomical regions, followed by cropping the original image to focus subsequent processing on the anatomical regions. The specialist networks are coupled through an attention mechanism that performs cross-attention to interlink features from different anatomies, serving as a soft relative shape prior. Central to our approach is an additive attention block (E-2A block), which is used throughout our architecture thanks to its efficiency. The source code is available at1

The incidence of enlarged chondrons in normal and osteoarthritic human cartilage and their relative matrix density

OBJECTIVE: To quantitate changes in the pericellular matrix in osteoarthritic (OA) articular cartilage. DESIGN: Chondrons were enzymatically isolated from normal and OA human cartilage. The cross-sectional area of the chondrons were measured. After immunolabeling for keratan sulfate, type VI collagen and type II collagen, the relative matrix density was determined for different size classes of chondrons with quantitative fluorescence microscopy. RESULTS: For individual chondrons, the average cross-sectional area (344+/-28 microm(2), mean+/-SE) for the normal specimens was significantly smaller than the average area (439+/-30 microm(2)) for the OA specimens. Using 496 microm(2) (mean+2 SD of the normal area) as the cut-off for enlarged chondrons, 33% of individual OA chondrons were enlarged compared to 16% for the normal. Chondrons under 300 microm(2) had a significantly higher density of keratan sulfate and type VI collagen than larger chondrons, while chondrons over 400 microm(2) had similar matrix densities. CONCLUSIONS: There is a higher incidence of enlarged chondrons in OA cartilage than in normal cartilage. The enlargement may initially be due to hydrodynamic swelling but further increases in size are due to increased matrix deposition

Image segmentation using joint spatial-intensity-shape features: Application to CT lung nodule segmentation

Automatic segmentation of medical images is a challenging problem due to the complexity and variability of human anatomy, poor contrast of the object being segmented, and noise resulting from the image acquisition process. This paper presents a novel non-parametric feature analysis method for the segmentation of 3D medical lesions. The proposed algorithm combines 1) a volumetric shape feature (shape index) based on high-order partial derivatives; 2) mean shift clustering in a joint spatial-intensity-shape (JSIS) feature space; and 3) a modified expectation-maximization (MEM) algorithm on the mean shift mode map to merge the neighboring regions (modes). In such a scenario, the volumetric shape feature is integrated into the process of the segmentation algorithm. The joint spatial–intensity-shape features provide rich information for the segmentation of the anatomic structures or lesions (tumors). The proposed method has been evaluated on a clinical dataset of thoracic CT scans that contains 68 nodules. A volume overlap ratio between each segmented nodule and the ground truth annotation is calculated. Using the proposed method, the mean overlap ratio over all the nodules is 0.80. On visual inspection and using a quantitative evaluation, the experimental results demonstrate the potential of the proposed method. It can properly segment a variety of nodules including juxta-vascular and juxta-pleural nodules, which are challenging for conventional methods due to the high similarity of intensities between the nodules and their adjacent tissues. This approach could also be applied to lesion segmentation in other anatomies, such as polyps in the colon

Diagnosing and Preventing Instabilities in Recurrent Video Processing.

Recurrent models are a popular choice for video enhancement tasks such as video denoising or super-resolution. In this work, we focus on their stability as dynamical systems and show that they tend to fail catastrophically at inference time on long video sequences. To address this issue, we (1) introduce a diagnostic tool which produces input sequences optimized to trigger instabilities and that can be interpreted as visualizations of temporal receptive fields, and (2) propose two approaches to enforce the stability of a model during training: constraining the spectral norm or constraining the stable rank of its convolutional layers. We then introduce Stable Rank Normalization for Convolutional layers (SRN-C), a new algorithm that enforces these constraints. Our experimental results suggest that SRN-C successfully enforces stablility in recurrent video processing models without a significant performance loss

Enhanced detection in CT colonography using adaptive diffusion filtering

Computer-aided detection (CAD) is a computerized procedure in medical science that supports the medical team’s interpretations and decisions. CAD uses information from a medical imaging modality such as Computed Tomography to detect suspicious lesions. Algorithms to detect these lesions are based on geometrical models which can describe the local structures and thus provide potential region candidates. Geometrical descriptive models are very dependent on the data quality which may affect the false positive rates in CAD. In this paper we propose an efficient adaptive diffusion technique that adaptively controls the diffusion flux of the local structures in the data using robust statistics. The proposed method acts isotropically in the homogeneous regions and anisotropically in the vicinity of jump discontinuities. This method structurally enhances the data and makes the geometrical descriptive models robust. For the iterative solver, we use an efficient gradient descent flows solver based on a PDE formulation of the problem. The whole proposed strategy, which makes use of adaptive diffusion filter coupled with gradient descent flows has been developed and evaluated on clinical data in the application to colonic polyp detection in Computed Tomography Colonoscopy

The Need for Knowledge Extraction: Understanding Harmful Gambling Behavior with Neural Networks

Responsible gambling is a field of study that involves supporting gamblers so as to reduce the harm that their gambling activity might cause. Recently in the literature, machine learning algorithms have been introduced as a way to predict potentially harmful gambling based on patterns of gambling behavior, such as trends in amounts wagered and the time spent gambling. In this paper, neural network models are analyzed to help predict the outcome of a partial proxy for harmful gambling behavior: when a gambler “self-excludes”, requesting a gambling operator to prevent them from accessing gambling opportunities. Drawing on survey and interview insights from industry and public officials as to the importance of interpretability, a variant of the knowledge extraction algorithm TREPAN is proposed which can produce compact, human-readable logic rules efficiently, given a neural network trained on gambling data. To the best of our knowledge, this paper reports the first industrial-strength application of knowledge extraction from neural networks, which otherwise are black-boxes unable to provide the explanatory insights which are crucially required in this area of application. We show that through knowledge extraction one can explore and validate the kinds of behavioral and demographic profiles that best predict self-exclusion, while developing a machine learning approach with greater potential for adoption by industry and treatment providers. Experimental results reported in this paper indicate that the rules extracted can achieve high fidelity to the trained neural network while maintaining competitive accuracy and providing useful insight to domain experts in responsible gambling

DeepFMRI: And End-to-End Deep Network for Classification of FRMI Data

With recent advancements in machine learning, the research community has made tremendous advances towards the classification of neurological disorders from time-series functional MRI signals. However, existing classification techniques rely on hand-crafted features and classical machine learning models. In this paper, we propose an end-to-end model that utilizes the representation learning capability of deep learning to classify a neurological disorder from fMRI data. The proposed DeepFMRI model is comprised of three networks, namely (1) a feature extractor, (2) a similarity network, and (3) a classification network. The model takes fMRI raw time-series signals as input and outputs the predicted labels; and is trained end-to-end using back-propagation. Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative model outperforms previous state-of-the-art

FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from functional MRI

Investigation of functional brain connectivity patterns using functional MRI has received significant interest in the neuroimaging domain. Brain functional connectivity alterations have widely been exploited for diagnosis and prediction of various brain disorders. Over the last several years, the research community has made tremendous advancements in constructing brain functional connectivity from timeseries functional MRI signals using computational methods. However, even modern machine learning techniques rely on conventional correlation and distance measures as a basic step towards the calculation of the functional connectivity. Such measures might not be able to capture the latent characteristics of raw time-series signals. To overcome this shortcoming, we propose a novel convolutional neural network based model, FCNet, that extracts functional connectivity directly from raw fMRI time-series signals. The FCNet consists of a convolutional neural network that extracts features from time-series signals and a fully connected network that computes the similarity between the extracted features in a Siamese architecture. The functional connectivity computed using FCNet is combined with phenotypic information and used to classify individuals as healthy controls or neurological disorder subjects. Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative framework can improve classification accuracy, which indicates that the features learnt from FCNet have superior discriminative power

Pearling: stroke segmentation with crusted pearl strings

We introduce a novel segmentation technique, called Pearling, for the semi-automatic extraction of idealized models of networks of strokes (variable width curves) in images. These networks may for example represent roads in an aerial photograph, vessels in a medical scan, or strokes in a drawing. The operator seeds the process by selecting representative areas of good (stroke interior) and bad colors. Then, the operator may either provide a rough trace through a particular path in the stroke graph or simply pick a starting point (seed) on a stroke and a direction of growth. Pearling computes in realtime the centerlines of the strokes, the bifurcations, and the thickness function along each stroke, hence producing a purified medial axis transform of a desired portion of the stroke graph. No prior segmentation or thresholding is required. Simple gestures may be used to trim or extend the selection or to add branches. The realtime performance and reliability of Pearling results from a novel disk-sampling approach, which traces the strokes by optimizing the positions and radii of a discrete series of disks (pearls) along the stroke. A continuous model is defined through subdivision. By design, the idealized pearl string model is slightly wider than necessary to ensure that it contains the stroke boundary. A narrower core model that fits inside the stroke is computed simultaneously. The difference between the pearl string and its core contains the boundary of the stroke and may be used to capture, compress, visualize, or analyze the raw image data along the stroke boundary

DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI

Background: Resting state fMRI has emerged as a popular neuroimaging method for automated recognition and classification of brain disorders. Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders affecting young children, yet its underlying mechanism is not completely understood and its diagnosis is mainly dependent on behaviour analysis. New method: In this paper, we propose an end-to-end deep learning architecture to diagnose ADHD. Our aim is to (1) automatically classify a subject as ADHD or healthy control, and (2) demonstrate the importance of functional connectivity to increase classification accuracy and provide interpretable results. The proposed method, called DeepFMRI, is comprised of three sequential networks, namely (1) a feature extractor, (2) a functional connectivity network, and (3) a classification network. The model takes fMRI pre-processed time-series signals as input and outputs a diagnosis, and is trained end-to-end using back-propagation. Results: Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative method outperforms previous state-of-the-art. Different imaging sites contributed the data to the ADHD-200 dataset. For the New York University imaging site, our proposed method was able to achieve classification accuracy of 73.1% (specificity 91.6%, sensitivity 65.5%). Comparison with Existing Methods: In this work, we propose a novel end-to-end deep learning method incorporating functional connectivity for the classification of ADHD. To the best of our knowledge, this has not been explored by existing studies. Conclusions: The results suggest that the proposed end-to-end deep learning architecture achieves better performance as compared to the other state-of-the-art methods. The findings suggest that the frontal lobe contains the most discriminative power towards the classification of ADHD