Differentiation of intracardiac tumors and thrombi by echocardiographic tissue characterization: comparison of an artificial neural network and human observers

Detection improvement of gliomas in hyperspectral imaging of protoporphyrin IX fluorescence - in vitro comparison of visual identification and machine thresholds

BACKGROUND

5-aminolevulinic acid (5-ALA) - precursor of protoporphyrin IX (PpIX) - is utilized in fluorescence guided surgery (FGS) of high-grade gliomas. PpIX is used to identify traces of glioma during resection. Visual inspection of the fluorescence seems inaccurate in comparison to optic techniques such as hyperspectral imaging (HSI).

AIM

To characterize the limits of PpIX fluorescence detection of (i) visual evaluation and (ii) HSI analysis and to (iii) develop a classification system for visible and non-visible PpIX fluorescence.

METHODS

Samples with increasing concentrations (C) of PpIX and non-fluorescent controls were evaluated using a surgical microscope under blue light illumination. Similar samples were imaged with a HSI system tuned to PpIX fluorescence peak wavelength (635 nm) and control (RGB) channels. Samples' intensities were defined, leading to 96 analysed pixels after batching.

RESULTS

Three expert neurosurgeons assessed the PpIX samples (n = 16) and controls (n = 8) with unanimous decisions (ICC = 0.704), resulting in 63% recognition rate, 48% sensitivity, 92% specificity, 92% positive predictive value (PPV) and 47% negative predictive value (NPV). HSI image analysis, comparing mean relative values, resulted in 96%, 100%, 86%, 94%, 100%, respectively. Minimum PpIX concentration detection for experts was 0.6-1.8 μmol/l and HSI's 0.03-0.15 μmol/l.

CONCLUSIONS

PpIX concentrations of low-grade gliomas, and those reported on glioblastoma infiltration zones, are below experts' detection threshold. HSI analysis exceeds the performance of expert's visual inspection nearly by 20-fold. Hybrid FGS-HSI systems should be investigated in parallel to long-term outcomes. Described methods are applicable as a standard for calibration, testing and development of subvisual FGS techniques.

Native-resolution myocardial principal Eulerian strain mapping using convolutional neural networks and Tagged Magnetic Resonance Imaging

BACKGROUND

Assessment of regional myocardial function at native pixel-level resolution can play a crucial role in recognizing the early signs of the decline in regional myocardial function. Extensive data processing in existing techniques limits the effective resolution and accuracy of the generated strain maps. The purpose of this study is to compute myocardial principal strain maps ε_p1 and ε_p2 from tagged MRI (tMRI) at the native image resolution using deep-learning local patch convolutional neural network (CNN) models (DeepStrain).

METHODS

For network training, validation, and testing, realistic tMRI datasets were generated and consisted of 53,606 cine images simulating the heart, the liver, blood pool, and backgrounds, including ranges of shapes, positions, motion patterns, noise, and strain. In addition, 102 in-vivo image datasets from three healthy subjects, and three Pulmonary Arterial Hypertension patients, were acquired and used to assess the network's in-vivo performance. Four convolutional neural networks were trained for mapping input tagging patterns to corresponding ground-truth principal strains using different cost functions. Strain maps using harmonic phase analysis (HARP) were obtained with various spectral filtering settings for comparison. CNN and HARP strain maps were compared at the pixel level versus the ground-truth and versus the least-loss in-vivo maps using Pearson correlation coefficients (R) and the median error and Inter-Quartile Range (IQR) histograms.

RESULTS

CNN-based local patch DeepStrain maps at a phantom resolution of 1.1mm × 1.1 mm and in-vivo resolution of 2.1mm × 1.6 mm were artifact-free with multiple fold improvement with ε_p1 ground-truth median error of 0.009(0.007) vs. 0.32(0.385) using HARP and ε_p2 ground-truth error of 0.016(0.021) vs. 0.181(0.08) using HARP. CNN-based strain maps showed substantially higher agreement with the ground-truth maps with correlation coefficients R > 0.91 for ε_p1 and ε_p2 compared to R < 0.21 and R < 0.82 for HARP-generated maps, respectively.

CONCLUSION

CNN-generated Eulerian strain mapping permits artifact-free visualization of myocardial function at the native image resolution.

Texture Spectrum Coupled with Entropy and Homogeneity Image Features for Myocardium Muscle Characterization

Background:

People in middle/later age often suffer from heart muscle damage due to coronary artery disease associated to myocardial infarction. In young people, the genetic forms of cardiomyopathies (heart muscle disease) are the utmost protuberant cause of myocardial disease.

Objective:

Accurate early detected information regarding the myocardial tissue structure is a key answer for tracking the progress of several myocardial diseases. associations while known disease-lncRNA associations are required only.

Method:

The present work proposes a new method for myocardium muscle texture classification based on entropy, homogeneity and on the texture unit-based texture spectrum approaches. Entropy and homogeneity are generated in moving windows of size 3x3 and 5x5 to enhance the texture features and to create the premise of differentiation of the myocardium structures. The texture is then statistically analyzed using the texture spectrum approach. Texture classification is achieved based on a fuzzy c–means descriptive classifier. The proposed method has been tested on a dataset of 80 echocardiographic ultrasound images in both short-axis and long-axis in apical two chamber view representations, for normal and infarct pathologies.

Results:

The noise sensitivity of the fuzzy c–means classifier was overcome by using the image features. The results established that the entropy-based features provided superior clustering results compared to homogeneity.

Conclusion:

Entropy image feature has a lower spread of the data in the clusters of healthy subjects and myocardial infarction. Also, the Euclidean distance function between the cluster centroids has higher values for both LAX and SAX views for entropy images.</P>

Application of neural networks for the analysis of intravascular ultrasound and histological aortic wall appearance-an in vitro tissue characterization study

The role of tissue characterization by intravascular ultrasound (IVUS) imaging of the aortic wall has not been well established. The artificial neural networks (ANNs) are a promising tool for image classification. The aim of the study was to assess the texture correlation between matching IVUS and histologic images of the aortic wall. The computer-based discrimination of pathology within the data sets was also evaluated. In vitro IVUS images and histologic sections from 36 aortic segments were compared using texture parameters that produced the best correlation or the highest discriminative value. The images were classified as normal or abnormal with variable degrees of pathology. Tissue characterization was performed by a nearest neighbor classifier, linear discriminant analysis (LDA) and the ANN-based approach. Good agreement was observed between IVUS and the histologic reference with a correlation coefficient of r = 0.89, r = 0.76 and r = 0.71 for the three most successful texture parameters. The ANN-based approach was the most effective in discriminant analysis, with a correct classification rate of 87.5% for histologic images and 79.2% for IVUS data. The study shows that ANNs are a potentially effective tool for assessment of IVUS aortic images.

Classification and segmentation of intracardiac masses in cardiac tumor echocardiograms

This paper describes an automatic method for classification and segmentation of different intracardiac masses in tumor echocardiograms. Identification of mass type is highly desirable, since to different treatment options for cardiac tumors (surgical resection) and thrombi (effective anticoagulant treatment) are possible. Correct diagnosis of the character of intracardiac mass in a living patient is a true challenge for a cardiologist; therefore, an objective image analysis method may be useful in heart diseases diagnosis. Image texture analysis is used to distinguish various types of masses. The presented methods assume that image texture encodes important histological features of masses and, therefore, texture numerical parameters enable the discrimination and segmentation of a mass. The recently developed technique based on the network of synchronized oscillators is proposed for the image segmentation. This technique is based on a 'temporary correlation' theory, which attempts to explain scene recognition as it would be performed by a human brain. This theory assumes that different groups of neural cells encode different properties of homogeneous image regions (e.g. shape, color, texture). Monitoring of temporal activity of cell groups leads to scene segmentation. A network of synchronized oscillators was successfully used for segmentation of Brodatz textures and medical textured images. The advantage of this network is its ability to detect texture boundaries. It can be also manufactured as a VLSI chip, for a very fast image segmentation. The accuracy of locating of analyzed tissues in the image should be assessed to evaluate a segmentation technique. The new evaluation method based on measurement of physical textured test objects was proposed. Firstly, a series of object images was obtained by the use of different devices (scanner, digital camera and TV camera). Secondly, the images were segmented using oscillator network and feedforward artificial neural network. Thirdly, geometrical test object parameters were estimated and compared to its true values. The experiment was repeated also for ultrasound images, which represented rectangular cross-section of synthetic sponge submerged in water. In addition, classification and segmentation of selected benign tumor echocardiograms were performed. Oscillator network was used with network weights defined for both whole texture region and texture boundary detection for the tumor segmentation. The latter method provides much faster segmentation with the similar accuracy. The obtained segmentation results were discussed and compared to the artificial neural network classifier. Finally, it was demonstrated that the network of synchronized oscillators is a reliable tool for the segmentation of the selected intracardiac masses, since it gives a relatively accurate location of analyzed tissues. The advantage of the proposed method is its resistance to changes of the visual information in the analyzed image and to noise and artifacts, often present in echocardiograms.

Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses

RATIONALE AND OBJECTIVE

To compare logistic regression and artificial neural network for computer-aided diagnosis on breast sonograms.

MATERIALS AND METHODS

Ultrasound images of 24 malignant and 30 benign masses were analyzed quantitatively for margin sharpness, margin echogenicity, and angular variation in margin. These features and age of patients were used with two pattern classifiers, logistic regression, and an artificial neural network to differentiate between malignant and benign masses. The performance of two methods was compared by receiver operating characteristic (ROC) analysis.

RESULTS

The area under the ROC curve Az (+/-SD) of the logistic regression analysis was 0.853 +/- 0.059 with 95% confidence limit (0.760-0.950). The area under the ROC curve of the artificial neural network analysis was 0.856 +/- 0.058 with 95% confidence limit (0.734-0.936). Although both the logistic regression and the artificial neural network had the same area under the ROC curve, the shapes of two curves were different. At 95% sensitivity, the artificial neural network had 76.5% specificity, whereas logistic regression had 64.7% specificity.

CONCLUSION

There was no difference in performance between logistic regression and the artificial neural network as measured by the area under the ROC curve. However, at a fixed 95% sensitivity, the artificial neural network had higher (12%) specificity compared with logistic regression value.

A right atrial mass: thrombus, tumour or other?

A 71-year-old man was admitted with right lower lobe pneumonia, right-sided pleural effusion and congestive cardiac failure. He had been well until a left occipito-parietal embolic stroke (documented on computed tomography; CT) 2 years previously. Chronic atrial fibrillation and hypertension were noted and he was commenced on digoxin, warfarin and amlodipine. A transthoracic echocardiogram showed normal left ventricular contractility, mild mitral regurgitation and a severely dilated left atrium. He made a full neurological recovery.