BACKGROUND: Automated algorithms may identify focal (FA) and rotational (RoA) activations during persistent atrial fibrillation (PeAF). OBJECTIVE: To evaluate an automated algorithm for characterizing and assessing significance of FA/RoA. METHODS: Eighty-six PeAF ablation patients (1411 maps) were analyzed. Maps were obtained with a 64-electrode basket using CARTOFINDER, which filters/annotates atrial unipolar electrograms over 30 seconds. Operators ablated FA/RoA followed by pulmonary vein isolation (PVI). The automated algorithm was retrospectively applied using QS patterns to identify FA and sequential activation gradients for RoA without phase mapping. Algorithm-identified FA and RoA were validated against blinded adjudicators. Ablation of algorithm-identified FA/RoA was related to procedural AF termination. RESULTS: 73% ± 18% of electrodes (65% ± 11% atrial surface area) were adequate for analysis. Compared with adjudicators, the algorithm had a sensitivity of 84% for FA and 86% for RoA. There were 4 ± 2 FA and 2 ± 2 RoA per patient. FA occurred 8 ± 6 times during the 30-second window (cumulative duration 8 ± 6 seconds). RoA occurred 5 ± 3 times (median 2, consecutive rotations) with a cumulative duration of 3 ± 2 seconds. Compared to patients without procedural AF termination, patients with termination had more FA ablated (75% vs 38%, P = 0.006). AF termination was not predicted by percentage of RoA ablated although there was a trend towards a higher percentage of left atrial RoA ablated ( P = 0.06). CONCLUSION: An automated algorithm had high sensitivity for FA and RoA. Acute AF termination was associated with FA ablation but not RoA ablation. Future studies need to define the significance of FA and RoA and whether they are overlapping or separate mechanisms.
- Keywords
- algorithm, focus, mapping, persistent atrial fibrillation, rotational activation,
- MeSH
- Algorithms * MeSH
- Electrophysiologic Techniques, Cardiac methods MeSH
- Atrial Fibrillation diagnosis physiopathology surgery MeSH
- Catheter Ablation adverse effects methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Body Surface Potential Mapping methods MeSH
- Prospective Studies MeSH
- Retrospective Studies MeSH
- Aged MeSH
- Pulmonary Veins surgery MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
In today's biometric and commercial settings, state-of-the-art image processing relies solely on artificial intelligence and machine learning which provides a high level of accuracy. However, these principles are deeply rooted in abstract, complex "black-box systems". When applied to forensic image identification, concerns about transparency and accountability emerge. This study explores the impact of two challenging factors in automated facial identification: facial expressions and head poses. The sample comprised 3D faces with nine prototype expressions, collected from 41 participants (13 males, 28 females) of European descent aged 19.96 to 50.89 years. Pre-processing involved converting 3D models to 2D color images (256 × 256 px). Probes included a set of 9 images per individual with head poses varying by 5° in both left-to-right (yaw) and up-and-down (pitch) directions for neutral expressions. A second set of 3,610 images per individual covered viewpoints in 5° increments from -45° to 45° for head movements and different facial expressions, forming the targets. Pair-wise comparisons using ArcFace, a state-of-the-art face identification algorithm yielded 54,615,690 dissimilarity scores. Results indicate that minor head deviations in probes have minimal impact. However, the performance diminished as targets deviated from the frontal position. Right-to-left movements were less influential than up and down, with downward pitch showing less impact than upward movements. The lowest accuracy was for upward pitch at 45°. Dissimilarity scores were consistently higher for males than for females across all studied factors. The performance particularly diverged in upward movements, starting at 15°. Among tested facial expressions, happiness and contempt performed best, while disgust exhibited the lowest AUC values.
- Keywords
- Automated algorithms, Facial expressions, Forensic image identification, Head pose,
- MeSH
- Algorithms * MeSH
- Automated Facial Recognition * methods MeSH
- Biometric Identification methods MeSH
- Adult MeSH
- Head Movements physiology MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Face anatomy & histology MeSH
- Image Processing, Computer-Assisted methods MeSH
- Posture physiology MeSH
- Facial Expression * MeSH
- Imaging, Three-Dimensional MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
The digital polymerase chain reaction (dPCR) is an irreplaceable variant of PCR techniques due to its capacity for absolute quantification and detection of rare deoxyribonucleic acid (DNA) sequences in clinical samples. Image processing methods, including micro-chamber positioning and fluorescence analysis, determine the reliability of the dPCR results. However, typical methods demand high requirements for the chip structure, chip filling, and light intensity uniformity. This research developed an image-to-answer algorithm with single fluorescence image capture and known image-related error removal. We applied the Hough transform to identify partitions in the images of dPCR chips, the 2D Fourier transform to rotate the image, and the 3D projection transformation to locate and correct the positions of all partitions. We then calculated each partition's average fluorescence amplitudes and generated a 3D fluorescence intensity distribution map of the image. We subsequently corrected the fluorescence non-uniformity between partitions based on the map and achieved statistical results of partition fluorescence intensities. We validated the proposed algorithms using different contents of the target DNA. The proposed algorithm is independent of the dPCR chip structure damage and light intensity non-uniformity. It also provides a reliable alternative to analyze the results of chip-based dPCR systems.
- MeSH
- Algorithms MeSH
- DNA * genetics MeSH
- Image Processing, Computer-Assisted * MeSH
- Polymerase Chain Reaction MeSH
- Reproducibility of Results MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- DNA * MeSH
We present a new algorithm to analyse information content in images acquired using automated fluorescence microscopy. The algorithm belongs to the group of autofocusing methods, but differs from its predecessors in that it can handle thick specimens and operate also in confocal mode. It measures the information content in images using a 'content function', which is essentially the same concept as a focus function. Unlike previously presented algorithms, this algorithm tries to find all significant axial positions in cases where the content function applied to real data is not unimodal, which is often the case. This requirement precludes using algorithms that rely on unimodality. Moreover, choosing a content function requires careful consideration, because some functions suppress local maxima. First, we test 19 content functions and evaluate their ability to show local maxima clearly. The results show that only six content functions succeed. To save time, the acquisition procedure needs to vary the step size adaptively, because a wide range of possible axial positions has to be passed so as not to miss a local maximum. The algorithm therefore has to assess the steepness of the content function online so that it can decide to use a bigger or smaller step size to acquire the next image. Therefore, the algorithm needs to know about typical behaviour of content functions. We show that for normalized variance, one of the most promising content functions, this knowledge can be obtained after normalizing with respect to the theoretical maximum of this function, and using hierarchical clustering. The resulting algorithm is more reliable and efficient than a simple procedure with constant steps.
- MeSH
- Algorithms * MeSH
- Cell Nucleus ultrastructure MeSH
- Fibroblasts ultrastructure MeSH
- Microscopy, Fluorescence methods MeSH
- Humans MeSH
- Online Systems MeSH
- Image Processing, Computer-Assisted methods MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
AIMS: Previous studies have demonstrated substantial variability in manual assessment of QRS complex duration (QRSd). Disagreements in QRSd measurements were also found in several automated algorithms tested on digitized electrocardiogram (ECG) recordings. The aim of our study was to investigate the variability of automated QRSd measurements performed by two commercially available electrocardiographs. METHODS AND RESULTS: Two GE MAC 5000 (GE-1 and GE-2) electrocardiographs and two Mortara ELI 350 (Mortara-1 and Mortara-2) electrocardiographs were used in the study. Participants for the study were recruited from patients hospitalized in the department of cardiology of a university hospital. Participants underwent up to four recording sessions within a single day with a different electrocardiograph at each session when two to four immediately successive ECG recordings were undertaken. In 76 patients, 683 ECGs were recorded; the mean QRSd was 109.0 ± 26.1 ms. The QRSd difference ≥10 ms between the first and second intra-session ECG was found in 7, 3, 20, and 14% of ECG pairs for GE-1, GE-2, Mortara-1, and Mortara-2, respectively. No inter-session difference in QRSd was found within both manufacturers. In individual patients, Mortara calculated the mean QRSd to be longer by 7.3 ms (95% CI: 6.2-8.5 ms, P < 0.0001) with a 2.1-times (95% CI: 1.9-2.4) greater standard deviation of the mean QRSd (7.1 vs. 3.3 ms, P < 0.001). CONCLUSION: Electrocardiographs from two manufacturers measured QRSd values with a systematic difference and a significantly different level of precision. This may have important clinical implications in selection of suitable candidates for cardiac resynchronization therapy.
- Keywords
- Automated measurement, ECG, QRS complex,
- MeSH
- Algorithms * MeSH
- Equipment Failure Analysis MeSH
- Equipment Design MeSH
- Diagnosis, Computer-Assisted instrumentation methods MeSH
- Electrocardiography instrumentation methods MeSH
- Humans MeSH
- Reproducibility of Results MeSH
- Pattern Recognition, Automated methods MeSH
- Aged MeSH
- Sensitivity and Specificity MeSH
- Check Tag
- Humans MeSH
- Aged MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study MeSH
- Comparative Study MeSH
Knowledge of soft tissue fiber structure is necessary for accurate characterization and modeling of their mechanical response. Fiber configuration and structure informs both our understanding of healthy tissue physiology and of pathological processes resulting from diseased states. This study develops an automatic algorithm to simultaneously estimate fiber global orientation, abundance, and waviness in an investigated image. To our best knowledge, this is the first validated algorithm which can reliably separate fiber waviness from its global orientation for considerably wavy fibers. This is much needed feature for biological tissue characterization. The algorithm is based on incremental movement of local regions of interest (ROI) and analyzes two-dimensional images. Pixels belonging to the fiber are identified in the ROI, and ROI movement is determined according to local orientation of fiber within the ROI. The algorithm is validated with artificial images and ten images of porcine trachea containing wavy fibers. In each image, 80-120 fibers were tracked manually to serve as verification. The coefficient of determination R2 between curve lengths and histograms documenting the fiber waviness and global orientation were used as metrics for analysis. Verification-confirmed results were independent of image rotation and degree of fiber waviness, with curve length accuracy demonstrated to be below 1% of fiber curved length. Validation-confirmed median and interquartile range of R2, respectively, were 0.90 and 0.05 for curved length, 0.92 and 0.07 for waviness, and 0.96 and 0.04 for global orientation histograms. Software constructed from the proposed algorithm was able to track one fiber in about 1.1 s using a typical office computer. The proposed algorithm can reliably and accurately estimate fiber waviness, curve length, and global orientation simultaneously, moving beyond the limitations of prior methods.
- Keywords
- automated algorithm, collagen structure, fiber orientation, fiber waviness, image analysis, soft tissue analysis,
- MeSH
- Algorithms * MeSH
- Collagen MeSH
- Swine MeSH
- Software * MeSH
- Animals MeSH
- Check Tag
- Animals MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Collagen MeSH
In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.
- Keywords
- cell morphology, classification, coherence-controlled holographic microscopy, digital holographic microscopy, quantitative phase imaging, supervised machine learning,
- MeSH
- Algorithms MeSH
- Cells classification cytology MeSH
- Holography methods MeSH
- Humans MeSH
- Microscopy methods MeSH
- Pattern Recognition, Automated MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
The classification of bioimages plays an important role in several biological studies, such as subcellular localisation, phenotype identification and other types of histopathological examinations. The objective of the present study was to develop a computer-aided bioimage classification method for the classification of bioimages across nine diverse benchmark datasets. A novel algorithm was developed, which systematically fused the features extracted from nine different convolution neural network architectures. A systematic fusion of features boosts the performance of a classifier but at the cost of the high dimensionality of the fused feature set. Therefore, non-discriminatory and redundant features need to be removed from a high-dimensional fused feature set to improve the classification performance and reduce the time complexity. To achieve this aim, a method based on analysis of variance and evolutionary feature selection was developed to select an optimal set of discriminatory features from the fused feature set. The proposed method was evaluated on nine different benchmark datasets. The experimental results showed that the proposed method achieved superior performance, with a significant reduction in the dimensionality of the fused feature set for most bioimage datasets. The performance of the proposed feature selection method was better than that of some of the most recent and classical methods used for feature selection. Thus, the proposed method was desirable because of its superior performance and high compression ratio, which significantly reduced the computational complexity.
- Keywords
- Bioimage classification, Convolutional neural networks, Evolutionary algorithms, Feature fusion, Pre-trained CNNs, Transfer learning,
- MeSH
- Algorithms * MeSH
- Neural Networks, Computer * MeSH
- Publication type
- Journal Article MeSH
The process of manual species identification is a daunting task, so much so that the number of taxonomists is seen to be declining. In order to assist taxonomists, many methods and algorithms have been proposed to develop semi-automated and fully automated systems for species identification. While semi-automated tools would require manual intervention by a domain expert, fully automated tools are assumed to be not as reliable as manual or semiautomated identification tools. Hence, in this study we investigate the accuracy of fully automated and semi-automated models for species identification. We have built fully automated and semi-automated species classification models using the monogenean species image dataset. With respect to monogeneans' morphology, they are differentiated based on the morphological characteristics of haptoral bars, anchors, marginal hooks and reproductive organs (male and female copulatory organs). Landmarks (in the semi-automated model) and shape morphometric features (in the fully automated model) were extracted from four monogenean species images, which were then classified using k-nearest neighbour and artificial neural network. In semi-automated models, a classification accuracy of 96.67 % was obtained using the k-nearest neighbour and 97.5 % using the artificial neural network, whereas in fully automated models, a classification accuracy of 90 % was obtained using the k-nearest neighbour and 98.8 % using the artificial neural network. As for the crossvalidation, semi-automated models performed at 91.2 %, whereas fully automated models performed slightly higher at 93.75 %.
A SEM/EDX based automated measurement and classification algorithm was tested as a method for the in-depth analysis of micro-environments in the Munich subway using a custom build mobile measurements system. Sampling was conducted at platform stations, to investigate the personal exposure of commuters to subway particulate matter during platform stays. EDX spectra and morphological features of all analyzed particles were automatically obtained and particles were automatically classified based on pre-defined chemical and morphological boundaries. Source apportionment for individual particles, such as abrasion processes at the wheel-brake interface, was partially possible based on the established particle classes. An average of 98.87 ± 1.06 % of over 200,000 analyzed particles were automatically assigned to the pre-defined classes, with 84.68 ± 16.45 % of particles classified as highly ferruginous. Manual EDX analysis further revealed, that heavy metal rich particles were also present in the ultrafine size range well below 100 nm.
