Roadmaps for Guiding Chest Computed Tomography Interpretation involving Pneumonia.

CT scanning of the chest is one the most important imaging modalities available for pulmonary disease diagnosis. Lung segmentation plays a crucial step in the pipeline of computer-aided analysis and diagnosis. As deep learning models have achieved human-level accuracy in semantic segmentation of anatomical structures, we propose to use trained deep learning models to predict both healthy and infectious areas in chest CT slices. The semantic segmentation results are summarized and visualized using volume rendering technology in the form of roadmaps. The roadmaps consist of both location and volume information that can be used as a location guidance for inspecting suspected pulmonary lesions of chest CT and can possibly be combined into a rapid triage algorithm for treating acute pulmonary diseases.Clinical Relevance- This research applied trained semantic segmentation models in identifying normal lung and pneumonic infection areas to generate a roadmap for assisting medical doctors in browsing chest CT and prognostication.

Measurement of Body Surface Area for Psoriasis Using U-net Models.

During the evaluation of body surface area (BSA), precise measurement of psoriasis is crucial for assessing disease severity and modulating treatment strategies. Physicians usually evaluate patients subjectively through direct visual evaluation. However, judgment based on the naked eye is not reliable. This study is aimed at evaluating the use of machine learning methods, specifically U-net models, and developing an artificial neural network prediction model for automated psoriasis lesion segmentation and BSA measurement. The segmentation of psoriasis lesions using deep learning is adopted to measure the BSA of psoriasis so that the severity can be evaluated automatically in patients. An automated psoriasis lesion segmentation method based on the U-net architecture was used with a focus on high-resolution images and estimation of the BSA. The proposed method trained the model with the same patch size of 512 × 512 and predicted testing images with different patch sizes. We collected 255 high-resolution psoriasis images representing large anatomical sites, such as the trunk and extremities. The average residual of the ground truth image and the predicted image was approximately 0.033. The interclass correlation coefficient between the U-net and dermatologist's segmentations measured in the ratio of affected psoriasis over the body area in the test dataset was 0.966 (95% CI: 0.981-0.937), indicating strong agreement. Herein, the proposed U-net model achieved dermatologist-level performance in estimating the involved BSA for psoriasis.

Time to peak and full width at half maximum in MR perfusion: valuable indicators for monitoring moyamoya patients after revascularization.

Moyamoya disease (MMD) is a chronic, steno-occlusive cerebrovascular disorder of unknown etiology. Surgical treatment is the only known effective method to restore blood flow to affected areas of the brain. However, there are lack of generally accepted noninvasive tools for therapeutic outcome monitoring. As dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) is the standard MR perfusion imaging technique in the clinical setting, we investigated a dataset of nineteen pediatric MMD patients with one preoperational and multiple periodic DSC MRI examinations for four to thirty-eight months after indirect revascularization. A rigid gamma variate model was used to derive two nondeconvolution-based perfusion parameters: time to peak (TTP) and full width at half maximum (FWHM) for monitoring transitional bolus delay and dispersion changes respectively. TTP and FWHM values were normalized to the cerebellum. Here, we report that 74% (14/19) of patients improve in both TTP and FWHM measurements, and whereof 57% (8/14) improve more noticeably on FWHM. TTP is in good agreement with Tmax in estimating bolus delay. Our study data also suggest bolus dispersion estimated by FWHM is an additional, informative indicator in pediatric MMD monitoring.

Multiphase Computed Tomographic Angiography with Bone Subtraction Using 3D Multichannel Convolution Neural Networks.

Multiphase computed tomographic angiography (CTA) have been demonstrated to be a reliable imaging tool for evaluating cerebral collateral circulation that can be used to select acute ischemic patients for recanalization therapy. We proposed using bone subtraction techniques to visualize multiphase CTA for clinicians to make fast and consistent decisions in the imaging triage of acute stroke patients. A total of 40 multiphase brain CTA datasets were collected and processed by two bone subtraction methods. The reference method used pre-contrast (phase 0) scans to create ground truth bone masks by thresholding. The tested method used only contrast enhanced (phases 1, 2, and 3) scans to extract bone masks with two versions (U-net and atrous) of 3D multichannel convolution neural networks (CNNs) in a supervised deep learning paradigm for semantic segmentation. Half (n = 20) of the datasets were used to train and half (n = 20) were used to test the conventional 3D U-net and a patch-based 3D multichannel atrous CNN. The tested U-net and atrous CNNs achieved a mean intersection over union (IoU) scores of 90.0% +/- 2.2 and 93.9% +/- 1.2 respectively.Clinical Relevance-This bone subtraction technique helps to visualize CTA volumetric datasets in the form of full brain angiogram-like images to assist the clinicians in the emergency department for evaluating acute ischemic stroke patients.

Volume Visualization for Improving CT Lung Nodule Detection.

Inspired by the outstanding performance of deep convolutional neural networks (CNNs), nowadays modern computer-aided detection (CAD) systems for CT lung nodules generally delve into 2D or 3D CNNs directly without considering traditional image preprocessing techniques. However, detection of large pulmonary nodules and masses are computationally challenging, especially for 3D CNNs. In this paper, we examine the possibility of using volume visualized CT thin-slab images with 2D CNNs to reduce computation complexity and improve CAD performance. We tested 4 types of images: original 2D CT, 2D projection of thin slabs, mixture by arranging original and projection in different color channels, and mixture by the pixelwise maximum intensity of original CT and projection. We evaluated these images on a dataset of 30 CT scans with 30 different-sized nodules and masses on GoogLeNet via a transfer learning and cross validation paradigm. We found that projection visualization alone had a better or equal area-under curve score for all the different-sized nodules and masses. However, mixture by the maximum of CT and projection demonstrated a preferred performance with a true positive rate of 0.8 and a false positive rate of 0.046 in detecting large nodules and masses.

Smooth bandpass empirical mode decomposition with rolling ball sifting for extracting carotid bruits and heart sounds.

Carotid bruits are systolic sounds associated with turbulent blood flow through atherosclerotic stenosis in the neck. They are audible intermittent high-frequency sounds mixed with low-frequency heart sounds that wax and wane periodically. It is a nontrivial problem to extract both bruits and heart sounds with high fidelity for further computer-aided analysis. In this paper we propose a smooth bandpass empirical mode decomposition (EMD) method to tackle the problem in the time domain. First, bandpass EMD is achieved by using a rolling ball algorithm to sift the local extrema of chosen time-scales. Second, the local zero is smoothed by interpolation with a monotone piecewise cubic spline. Preliminary results indicate that the new method is able to extract both carotid bruits and heart sounds as visually smooth oscillating components.

Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms.

Sleep spindles are brief bursts of brain activity in the sigma frequency range (11-16 Hz) measured by electroencephalography (EEG) mostly during non-rapid eye movement (NREM) stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1) the lack of common benchmark databases, and (2) the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA), the Strength Pareto Evolutionary Algorithm (SPEA2), to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT), and two Hilbert-Huang transform (HHT) based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726-0.737.

Rolling ball sifting algorithm for the augmented visual inspection of carotid bruit auscultation.

Carotid bruits are systolic sounds associated with turbulent blood flow through atherosclerotic stenosis in the neck. They are audible intermittent high-frequency (above 200 Hz) sounds mixed with background noise and transmitted low-frequency (below 100 Hz) heart sounds that wax and wane periodically. It is a nontrivial task to extract both bruits and heart sounds with high fidelity for further computer-aided auscultation and diagnosis. In this paper we propose a rolling ball sifting algorithm that is capable to filter signals with a sharper frequency selectivity mechanism in the time domain. By rolling two balls (one above and one below the signal) of a suitable radius, the balls are large enough to roll over bruits and yet small enough to ride on heart sound waveforms. The high-frequency bruits can then be extracted according to a tangibility criterion by using the local extrema touched by the balls. Similarly, the low-frequency heart sounds can be acquired by a larger radius. By visualizing the periodicity information of both the extracted heart sounds and bruits, the proposed visual inspection method can potentially improve carotid bruit diagnosis accuracy.

The feasibility of using manual segmentation in a multifeature computer-aided diagnosis system for classification of skin lesions: a retrospective comparative study.

OBJECTIVES: To investigate the feasibility of manual segmentation by users of different backgrounds in a previously developed multifeature computer-aided diagnosis (CADx) system to classify melanocytic and non-melanocytic skin lesions based on conventional digital photographic images. METHODS: In total, 347 conventional photographs of melanocytic and non-melanocytic skin lesions were retrospectively reviewed, and manually segmented by two groups of physicians, dermatologists and general practitioners, as well as by an automated segmentation software program, JSEG. The performance of CADx based on inputs from these two groups of physicians and that of the JSEG program was compared using feature agreement analysis. RESULTS: The estimated area under the receiver operating characteristic curve for classification of benign or malignant skin lesions based were comparable on individual segmentation by the gold standard (0.893, 95% CI 0.856 to 0.930), dermatologists (0.886, 95% CI 0.863 to 0.908), general practitioners (0.883, 95% CI 0.864 to 0.903) and JSEG (0.856, 95% CI 0.812 to 0.899). The agreement in the malignancy probability scores among the physicians was excellent (intraclass correlation coefficient: 0.91). By selecting an optimal cut-off value of malignancy probability score, the sensitivity and specificity were 80.07% and 81.47% for dermatologists and 79.90% and 80.20% for general practitioners. CONCLUSIONS: This study suggests that manual segmentation by general practitioners is feasible in the described CADx system for classifying benign and malignant skin lesions.

Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study.

BACKGROUND: Computer-aided diagnosis (CADx) software that provides a second opinion has been widely used to assist physicians with various tasks. In dermatology, however, CADx has been mostly limited to melanoma or melanocytic skin cancer diagnosis. The frequency of non-melanocytic skin cancers and the accessibility of regular digital macrographs have raised interest in developing CADx for broader applications. OBJECTIVES: To investigate the feasibility of using CADx to diagnose both melanocytic and non-melanocytic skin lesions based on conventional digital photographic images. METHODS: This study was approved by an institutional review board, and the requirement to obtain informed consent was waived. In total, 769 conventional photographs of melanocytic and non-melanocytic skin lesions were retrospectively reviewed and used to develop a CADx system. Conventional and new color-related image features were developed to classify the lesions as benign or malignant using support vector machines (SVMs). The performance of CADx was compared with that of dermatologists. RESULTS: The clinicians' overall sensitivity, specificity, and accuracy were 83.33%, 85.88%, and 85.31%, respectively. New color correlation and principal component analysis (PCA) features improved the classification ability of the baseline CADx (p = 0.001). The estimated area under the receiver operating characteristic (ROC) curve (Az) of the proposed CADx system was 0.949, with a sensitivity and specificity of 85.63% and 87.65%, respectively, and a maximum accuracy of 90.64%. CONCLUSIONS: We have developed an effective CADx system to classify both melanocytic and non-melanocytic skin lesions using conventional digital macrographs. The system's performance was similar to that of dermatologists at our institute. Through improved feature extraction and SVM analysis, we found that conventional digital macrographs were feasible for providing useful information for CADx applications. The new color-related features significantly improved CADx applications for skin cancer.

A robust hair segmentation and removal approach for clinical images of skin lesions.

Artifacts such as hair are major obstacles to automatic segmentation of pigmented skin lesion images for computer-aided diagnosis systems. It is even more challenging to process clinical images taken by a regular digital camera, where the shadows of the skin texture may mimic hair-like curvilinear structures. In this study, we examined the popular DullRazor software with a dataset of 20 clinical images. The software, specifically designed for dermoscopic images, was unable to remove fine hairs or hairs in the shade. Alternatively, we proposed using conventional matched filters to enhance curvilinear structures. The more complicate hair intersection patterns, which were known to generate low matched filtering responses, were recovered by using region growing algorithms from nearby detected hair segments with linear discriminant analysis (LDA) based on a color similarity criterion. The preliminary results indicated the proposed method was able to remove more fine hairs and hairs in the shade, and lower false hair detection rate by 58% (from 0.438 to 0.183) as compared to the DullRazor's approach.

Intracranial dural arteriovenous fistulas: diagnosis and evaluation with 64-detector row CT angiography.

PURPOSE: To analyze the diagnostic effectiveness and application of computed tomographic (CT) angiography by using a new algorithm (hybrid CT angiography) in dural arteriovenous fistulas (AVFs). MATERIALS AND METHODS: Institutional review board approval was obtained for retrospectively postprocessing the raw data from CT angiography by using hybrid CT, which is a mixture of a bone subtraction and masking method for bone removal. The study included 22 patients with 24 dural AVFs and 14 control subjects. The grades in patients with dural AVF determined with hybrid CT angiography and digital subtraction angiography (DSA) were compared, and hybrid CT angiography was applied as a tool for planning endovascular treatment. The adjusted Wald method was used to estimate confidence intervals (CIs), and the Cohen kappa statistic was used to assess interobserver agreement. RESULTS: Hybrid CT angiography in the 24 dural AVFs revealed asymmetric sinus enhancement in 22 lesions (92%), engorged arteries in 19 (79%), transosseous enhanced vessels in 19 (79%), engorged extracranial veins in 13 (54%), engorged cortical veins in seven (29%), and sinus thrombosis in four (17%). In all 24 lesions, at least two of six imaging signs for diagnosis of dural AVFs were present. The kappa test analysis revealed a high level of interobserver agreement (kappa, 0.56-1.00) in reading the diagnostic imaging signs. The observed agreement between DSA and readers was 100% in the cavernous sinus region and in hypoglossal and clival lesions and 78%-89% in the transverse sigmoid sinus. The overall sensitivity, specificity, positive predictive value, and negative predictive value were 0.93 (95% CI: 0.85, 0.97), 0.98 (95% CI: 0.93, 1.00), 0.97 (95% CI: 0.90, 0.99), and 0.95 (95% CI: 0.90, 0.98), respectively. CONCLUSION: Hybrid CT angiography is a promising tool for the diagnosis of dural AVF. It can provide key information necessary for treatment planning.

Improving Polyp Detection Algorithms for CT Colonography: Pareto Front Approach.

We investigated a Pareto front approach to improving polyp detection algorithms for CT colonography (CTC). A dataset of 56 CTC colon surfaces with 87 proven positive detections of 53 polyps sized 4 to 60 mm was used to evaluate the performance of a one-step and a two-step curvature-based region growing algorithm. The algorithmic performance was statistically evaluated and compared based on the Pareto optimal solutions from 20 experiments by evolutionary algorithms. The false positive rate was lower (p<0.05) by the two-step algorithm than by the one-step for 63% of all possible operating points. While operating at a suitable sensitivity level such as 90.8% (79/87) or 88.5% (77/87), the false positive rate was reduced by 24.4% (95% confidence intervals 17.9-31.0%) or 45.8% (95% confidence intervals 40.1-51.0%) respectively. We demonstrated that, with a proper experimental design, the Pareto optimization process can effectively help in fine-tuning and redesigning polyp detection algorithms.

Using standard nonenhanced axial scans for cerebral CT angiography bone elimination: feasibility study.

OBJECTIVE: To investigate the feasibility of using standard nonenhanced axial-mode scans as precontrast scans for bone elimination in cerebral CT angiography (CTA). MATERIALS AND METHODS: A consecutive dataset of 32 patients who had both cerebral nonenhanced CT (NECT) (scanned in axial mode) and subtraction CTA (scanned in helical mode) examinations between April and August 2008 were retrospectively analyzed. For each patient, both axial- and helical-mode, NECT scans were processed by using the matched mask bone elimination (MMBE) method. Bone masks generated from axial- and helical-mode NECT scans were quantitatively compared by using overlapping analyses. The diagnostic quality and noise level of the resultant, maximum intensity projection, images by using 2 different bone masks were visually evaluated by 2 neuroradiologists independently using a 5-point scale (inferior, 1; worse, 2; equivalent, 3; better, 4; superior, 5). The effective doses to patients were estimated by using a dose-length product method. RESULTS: Of the 28 (87.5%) patients without intrascan movements, overlap rates between axial- and helical-mode bone masks ranged from 99.2% to 99.9% (mean, 99.7% +/- 0.2%). The mean diagnostic quality and noise level scores of resultant maximum intensity projection images given by 2 neuroradiologists were 3.0 +/- 0.3 and 2.5 +/- 0.5, respectively. The effective dose to patients with a routine brain CTA examination can be reduced from 1.16 to 0.78 mSv (16 cm, field-of-view) by using the proposed method if standard axial-mode NECT scans of the head are readily available. CONCLUSION: We found that using standard axial-mode NECT scans for bone elimination in helical-mode CTA is feasible. This method can further lower radiation dose without compromising the diagnostic quality.

Wetting behaviors of individual nanostructures.

Pinning of a liquid contact line by micro/nanoscale defects is attributed as the physical origin of macroscopic contact angle hysteresis. However, direct experimental quantification of the pinning effect at the nanoscale has yet to be fully explored to establish this link. Here we present an experimental technique to systematically investigate the wetting behaviors of individual hydrophilic nanostructures with diameters from 2000 nm down to 75 nm. Our results show that the macroscopic pinning behavior is preserved for nanostructures with dimensions down to approximately 200 nm. In addition, the estimated depinning liquid contact angle at the nanoscale is in agreement with the macroscopic receding contact angle, which indicates a physical link between nanoscopic pinning to the macroscopic liquid receding phenomenon.

Optimizing computer-aided colonic polyp detection for CT colonography by evolving the Pareto fronta.

A multiobjective genetic algorithm is designed to optimize a computer-aided detection (CAD) system for identifying colonic polyps. Colonic polyps appear as elliptical protrusions on the inner surface of the colon. Curvature-based features for colonic polyp detection have proved to be successful in several CT colonography (CTC) CAD systems. Our CTC CAD program uses a sequential classifier to form initial polyp detections on the colon surface. The classifier utilizes a set of thresholds on curvature-based features to cluster suspicious colon surface regions into polyp candidates. The thresholds were previously chosen experimentally by using feature histograms. The chosen thresholds were effective for detecting polyps sized 10 mm or larger in diameter. However, many medium-sized polyps, 6-9 mm in diameter, were missed in the initial detection procedure. In this paper, the task of finding optimal thresholds as a multiobjective optimization problem was formulated, and a genetic algorithm to solve it was utilized by evolving the Pareto front of the Pareto optimal set. The new CTC CAD system was tested on 792 patients. The sensitivities of the optimized system improved significantly, from 61.68% to 74.71% with an increase of 13.03% (95% CI [6.57%, 19.5%], p = 7.78 x 10(-5)) for the size category of 6-9 mm polyps, from 65.02% to 77.4% with an increase of 12.38% (95% CI [6.23%, 18.53%], p = 7.95 x 10(-5)) for polyps 6 mm or larger, and from 82.2% to 90.58% with an increase of 8.38% (95% CI [0.75%, 16%], p = 0.03) for polyps 8 mm or larger at comparable false positive rates. The sensitivities of the optimized system are nearly equivalent to those of expert radiologists.

On concise 3-D simple point characterizations: a marching cubes paradigm.

The centerlines of tubular structures are useful for medical image visualization and computer-aided diagnosis applications. They can be effectively extracted by using a thinning algorithm that erodes an object layer by layer until only a skeleton is left. An object point is "simple" and can be safely deleted only if the resultant image is topologically equivalent to the original. Numerous characterizations of 3-D simple points based on digital topology already exist. However, little work has been done in the context of marching cubes (MC). This paper reviews several concise 3-D simple point characterizations in a MC paradigm. By using the Euler characteristic and a few newly observed properties in the context of connectivity-consistent MC, we present concise and more self-explanatory proofs. We also present an efficient method for computing the Euler characteristic locally for MC surfaces. Performance evaluations on different implementations are conducted on synthetic data and multidetector computed tomography examination of virtual colonoscopy and angiography.

Wavelet method for CT colonography computer-aided polyp detection.

Computed tomographic colonography (CTC) computer aided detection (CAD) is a new method to detect colon polyps. Colonic polyps are abnormal growths that may become cancerous. Detection and removal of colonic polyps, particularly larger ones, has been shown to reduce the incidence of colorectal cancer. While high sensitivities and low false positive rates are consistently achieved for the detection of polyps sized 1 cm or larger, lower sensitivities and higher false positive rates occur when the goal of CAD is to identify "medium"-sized polyps, 6-9 mm in diameter. Such medium-sized polyps may be important for clinical patient management. We have developed a wavelet-based postprocessor to reduce false positives for this polyp size range. We applied the wavelet-based postprocessor to CTC CAD findings from 44 patients in whom 45 polyps with sizes of 6-9 mm were found at segmentally unblinded optical colonoscopy and visible on retrospective review of the CT colonography images. Prior to the application of the wavelet-based postprocessor, the CTC CAD system detected 33 of the polyps (sensitivity 73.33%) with 12.4 false positives per patient, a sensitivity comparable to that of expert radiologists. Fourfold cross validation with 5000 bootstraps showed that the wavelet-based postprocessor could reduce the false positives by 56.61% (p <0.001), to 5.38 per patient (95% confidence interval [4.41, 6.34]), without significant sensitivity degradation (32/45, 71.11%, 95% confidence interval [66.39%, 75.74%], p=0.1713). We conclude that this wavelet-based postprocessor can substantially reduce the false positive rate of our CTC CAD for this important polyp size range.

Teniae coli-based circumferential localization system for CT colonography: feasibility study.

This HIPAA-compliant study, with institutional review board approval and informed patient consent, was conducted to retrospectively develop a teniae coli-based circumferential localization method for guiding virtual colon navigation and colonic polyp registration. Colonic surfaces (n = 72) were depicted at computed tomographic (CT) colonography performed in 36 patients (26 men, 10 women; age range, 47-72 years) in the supine and prone positions. For 70 (97%) colonic surfaces, the tenia omentalis (TO), the most visible of the three teniae coli on a well-distended colonic surface, was manually extracted from the cecum to the descending colon. By virtually dissecting and flattening the colon along the TO, the authors developed a localization system involving 12 grid lines to estimate the circumferential positions of polyps. A sessile polyp would most likely (at 95% confidence level) be found within +/-1.2 grid lines (one grid line equals 1/12 the circumference) with use of the proposed method. By orienting and positioning the virtual cameras with use of the new localization system, synchronized prone and supine navigation was achieved. The teniae coli are extractable landmarks, and the teniae coli-based circumferential localization system helps guide virtual navigation and polyp registration at CT colonography.