AngioNet: a convolutional neural network for vessel segmentation in X-ray angiography.

Coronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye is flushed through the coronary vessels to visualize the severity of vessel narrowing, or stenosis. Cardiologists typically use visual estimation to approximate the percent diameter reduction of the stenosis, and this directs therapies like stent placement. A fully automatic method to segment the vessels would eliminate potential subjectivity and provide a quantitative and systematic measurement of diameter reduction. Here, we have designed a convolutional neural network, AngioNet, for vessel segmentation in X-ray angiography images. The main innovation in this network is the introduction of an Angiographic Processing Network (APN) which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, pixel accuracy 0.983, sensitivity 0.918, specificity 0.987). The purpose of the APN is to create an end-to-end pipeline for image pre-processing and segmentation, learning the best possible pre-processing filters to improve segmentation. We have also demonstrated the interchangeability of our network in measuring vessel diameter with Quantitative Coronary Angiography. Our results indicate that AngioNet is a powerful tool for automatic angiographic vessel segmentation that could facilitate systematic anatomical assessment of coronary stenosis in the clinical workflow.

Automated Segmentation and Severity Analysis of Subdural Hematoma for Patients with Traumatic Brain Injuries.

Detection and severity assessment of subdural hematoma is a major step in the evaluation of traumatic brain injuries. This is a retrospective study of 110 computed tomography (CT) scans from patients admitted to the Michigan Medicine Neurological Intensive Care Unit or Emergency Department. A machine learning pipeline was developed to segment and assess the severity of subdural hematoma. First, the probability of each point belonging to the hematoma region was determined using a combination of hand-crafted and deep features. This probability provided the initial state of the segmentation. Next, a 3D post-processing model was applied to evolve the initial state and delineate the hematoma. The recall, precision, and Dice similarity coefficient of the proposed segmentation method were 78.61%, 76.12%, and 75.35%, respectively, for the entire population. The Dice similarity coefficient was 79.97% for clinically significant hematomas, which compared favorably to an inter-rater Dice similarity coefficient. In volume-based severity analysis, the proposed model yielded an F1, recall, and specificity of 98.22%, 98.81%, and 92.31%, respectively, in detecting moderate and severe subdural hematomas based on hematoma volume. These results show that the combination of classical image processing and deep learning can outperform deep learning only methods to achieve greater average performance and robustness. Such a system can aid critical care physicians in reducing time to intervention and thereby improve long-term patient outcomes.

Utilization of smartphone and tablet camera photographs to predict healing of diabetes-related foot ulcers.

The objective of this study was to build a machine learning model that can predict healing of diabetes-related foot ulcers, using both clinical attributes extracted from electronic health records (EHR) and image features extracted from photographs. The clinical information and photographs were collected at an academic podiatry wound clinic over a three-year period. Both hand-crafted color and texture features and deep learning-based features from the global average pooling layer of ResNet-50 were extracted from the wound photographs. Random Forest (RF) and Support Vector Machine (SVM) models were then trained for prediction. For prediction of eventual wound healing, the models built with hand-crafted imaging features alone outperformed models built with clinical or deep-learning features alone. Models trained with all features performed comparatively against models trained with hand-crafted imaging features. Utilization of smartphone and tablet photographs taken outside of research settings hold promise for predicting prognosis of diabetes-related foot ulcers.

Midline Shift vs. Mid-Surface Shift: Correlation with Outcome of Traumatic Brain Injuries.

Traumatic brain injury (TBI) is a major health and socioeconomic problem globally that is associated with a high level of mortality. Early and accurate diagnosis and prognosis of TBI is important in patient management and preventing any secondary injuries. Computer tomography (CT) imaging assists physicians in diagnosing injury and guiding treatment. One of the clinical parameters extracted from CT images is midline shift, a measure of linear displacement in brain structure, which is correlated with TBI patient outcomes. However, only a tiny fraction of the overall tissue displacement is quantified through this parameter. In this paper, a novel measurement of overall mid-surface shift is proposed that quantifies the total volume of brain tissue shifted across the midline. When compared to traditional midline shift, mid-surface shift has a stronger correlation with TBI patient outcomes.

Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours.

Automated segmentation of the spleen in CT volumes is difficult due to variations in size, shape, and position of the spleen within the abdominal cavity as well as similarity of intensity values among organs in the abdominal cavity. In this paper we present a method for automated localization and segmentation of the spleen within axial abdominal CT volumes using trained classification models, active contours, anatomical information, and adaptive features. The results show an average Dice score of 0.873 on patients experiencing various chest, abdominal, and pelvic traumas taken at different contrast phases.

Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network.

Colorectal cancer is one of the highest causes of cancer-related death, especially in men. Polyps are one of the main causes of colorectal cancer, and early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we proposed a polyp segmentation method based on the convolutional neural network. Two strategies enhance the performance of the method. First, we perform a novel image patch selection method in the training phase of the network. Second, in the test phase, we perform effective post-processing on the probability map that is produced by the network. Evaluation of the proposed method using the CVC-ColonDB database shows that our proposed method achieves more accurate results in comparison with previous colonoscopy video-segmentation methods.

Automatic Midline Shift Detection in Traumatic Brain Injury.

Fast and accurate midline shift (MLS) estimation has a significant impact on diagnosis and treatment of patients with Traumatic Brain Injury (TBI). In this paper, we propose an automated method to calculate the amount of shift in the midline structure of TBI patients. The MLS values were annotated by a neuroradiologist. We first select a number of slices among all the slices in a CT scan based on metadata as well as information extracted from the images. After the slice selection, we propose an efficient segmentation technique to detect the ventricles. We use the ventricular geometric patterns to calculate the actual midline and also anatomical information to detect the ideal midline. The distance between these two lines is used as an estimate of MLS. The proposed methods are applied on a TBI dataset where they show a significant improvement of the the proposed method upon existing approach.

Reversible Image Watermarking for Health Informatics Systems Using Distortion Compensation in Wavelet Domain.

Reversible image watermarking guaranties restoration of both original cover and watermark logo from the watermarked image. Capacity and distortion of the image under reversible watermarking are two important parameters. In this study, a reversible watermarking is investigated by focusing on increasing the embedding capacity and reducing the distortion in medical images. We use integer wavelet transform for embedding one bit of watermark in a transform coefficient. We devise a novel approach that when a coefficient is modified in one iteration, the produced distortion is compensated in the next iteration. This distortion compensation method would result in low distortion rate. The proposed method is tested on four types of medical images including MRI of the brain, cardiac MRI, MRI of breast and intestinal polyp images. The maximum capacity of 1.5 BPP is obtained by using a one-level wavelet transform. Experimental results demonstrate that the proposed method is superior to the stateof-the-art works in terms of capacity and distortion.

Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network.

Medical image analysis, especially segmenting a specific organ, has an important role in developing clinical decision support systems. In cardiac magnetic resonance (MR) imaging, segmenting the left and right ventricles helps physicians diagnose different heart abnormalities. There are challenges for this task, including the intensity and shape similarity between the left ventricle and other organs, inaccurate boundaries, and presence of noise in most of the images. In this paper, we propose an automated method for segmenting the left ventricle in cardiac MR images. We first automatically extract the region of interest and then employ it as an input of a fully convolutional network. We train the network accurately despite the small number of left ventricle pixels in comparison with the whole image. Thresholding on the output map of the fully convolutional network and selection of regions based on their roundness are performed in our proposed post-processing phase. The Dice score of our method reaches 87.24% by applying this algorithm on the York dataset of heart images.

Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background.

By increasing the volume of telemedicine information, the need for medical image compression has become more important. In angiographic images, a small ratio of the entire image usually belongs to the vasculature that provides crucial information for diagnosis. Other parts of the image are diagnostically less important and can be compressed with higher compression ratio. However, the quality of those parts affects the overall understanding of the image as well. Existing methods compress foreground and background of angiographic images using different techniques. In this paper, we first utilize a convolutional neural network to segment vessels and then represent a hierarchical block processing algorithm capable of both eliminating the background redundancies and preserving the overall visual quality of angiograms.

Vessel segmentation and catheter detection in X-ray angiograms using superpixels.

Coronary artery disease (CAD) is the leading cause of death around the world. One of the most common imaging methods for diagnosing CAD is the X-ray angiography (XRA). Diagnosing using XRA images is usually challenging due to some reasons such as, non-uniform illumination, low contrast, presence of other body tissues, and presence of catheter. These challenges make the diagnosis task hard and more prone to misdiagnosis. In this paper, we propose a new method for coronary artery segmentation, catheter detection, and centerline extraction in X-ray angiography images. For the segmentation, initially, three different superpixel scales are exploited, and a measure for vesselness probability of each superpixel is determined. A voting mechanism is used for obtaining an initial segmentation map from the three superpixel scales. The initial segmentation is refined by finding the orthogonal line on each ridge pixel of vessel region. The catheter is detected in the first frame of the angiography sequence and is tracked in other frames by fitting a second order polynomial on it. Also, we use the image ridges for extracting the coronary artery centerlines. We evaluated and compared our method with one of the previous well-known coronary artery segmentation methods on two challenging datasets. The results show that our method can segment the vessels and also detect and track the catheter in the XRA sequences. In general, the results assessed by a cardiologist show that 83% of the images processed by our proposed segmentation method were labeled as good or excellent, while this score for the compared method is 48%. Also, the evaluation results show that our method performs 67% faster than the compared method. Graphical abstract Proposed framework for coronary artery detection.

Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma.

PURPOSE: Computerized prescreening of suspicious moles and lesions for malignancy is of great importance for assessing the need and the priority of the removal surgery. Detection can be done by images captured by standard cameras, which are more preferable due to low cost and availability. One important step in computerized evaluation is accurate detection of lesion's region, i.e., segmentation of an image into two regions as lesion and normal skin. METHODS: In this paper, a new method based on deep neural networks is proposed for accurate extraction of a lesion region. The input image is preprocessed, and then, its patches are fed to a convolutional neural network. Local texture and global structure of the patches are processed in order to assign pixels to lesion or normal classes. A method for effective selection of training patches is proposed for more accurate detection of a lesion's border. RESULTS: Our results indicate that the proposed method could reach the accuracy of 98.7% and the sensitivity of 95.2% in segmentation of lesion regions over the dataset of clinical images. CONCLUSION: The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.

Digitization of Electrocardiogram From Telemetry Prior to In-hospital Cardiac Arrest: A Pilot Study.

BACKGROUND: Analyzing telemetry electrocardiogram (ECG) data over an extended period is often time-consuming because digital records are not widely available at hospitals. Investigating trends and patterns in the ECG data could lead to establishing predictors that would shorten response time to in-hospital cardiac arrest (I-HCA). This study was conducted to validate a novel method of digitizing paper ECG tracings from telemetry systems in order to facilitate the use of heart rate as a diagnostic feature prior to I-HCA. METHODS: This multicenter study used telemetry to investigate full-disclosure ECG papers of 44 cardiovascular patients obtained within 1 hr of I-HCA with initial rhythms of pulseless electrical activity and asystole. Digital ECGs were available for seven of these patients. An algorithm to digitize the full-disclosure ECG papers was developed using the shortest path method. The heart rate was measured manually (averaging R-R intervals) for ECG papers and automatically for digitized and digital ECGs. RESULTS: Significant correlations were found between manual and automated measurements of digitized ECGs (p < .001) and between digitized and digital ECGs (p < .001). Bland-Altman methods showed bias = .001 s, SD = .0276 s, lower and upper 95% limits of agreement for digitized and digital ECGs = .055 and -.053 s, and percentage error = 0.22%. Root mean square (rms), percentage rms difference, and signal to noise ratio values were in acceptable ranges. CONCLUSION: The digitization method was validated. Digitized ECG provides an efficient and accurate way of measuring heart rate over an extended period of time.

Big Data Analytics in Healthcare.

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.