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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.
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.
- MeSH
- algoritmy * MeSH
- elektrofyziologické techniky kardiologické metody MeSH
- fibrilace síní diagnóza patofyziologie chirurgie MeSH
- katetrizační ablace škodlivé účinky metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- mapování potenciálů tělesného povrchu metody MeSH
- prospektivní studie MeSH
- retrospektivní studie MeSH
- senioři MeSH
- venae pulmonales chirurgie MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- multicentrická studie 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.
- MeSH
- algoritmy * MeSH
- automatizované rozpoznávání obličeje * metody MeSH
- biometrická identifikace metody MeSH
- dospělí MeSH
- hlava - pohyby fyziologie MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- obličej anatomie a histologie MeSH
- počítačové zpracování obrazu metody MeSH
- postura těla fyziologie MeSH
- výraz obličeje * MeSH
- zobrazování trojrozměrné MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články 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.
- MeSH
- algoritmy MeSH
- buňky klasifikace cytologie MeSH
- holografie metody MeSH
- lidé MeSH
- mikroskopie metody MeSH
- rozpoznávání automatizované MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články 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.
- MeSH
- algoritmy * MeSH
- neuronové sítě * MeSH
- Publikační typ
- časopisecké články 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 %.
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.
- MeSH
- algoritmy * MeSH
- analýza selhání vybavení MeSH
- design vybavení MeSH
- diagnóza počítačová přístrojové vybavení metody MeSH
- elektrokardiografie přístrojové vybavení metody MeSH
- lidé MeSH
- reprodukovatelnost výsledků MeSH
- rozpoznávání automatizované metody MeSH
- senioři MeSH
- senzitivita a specificita MeSH
- Check Tag
- lidé MeSH
- senioři MeSH
- Publikační typ
- časopisecké články MeSH
- hodnotící studie MeSH
- srovnávací studie MeSH
The A* - Algorithm for heuristic search is applied to construct a Neural Network structure (NS) that optimally fits the structure of data to be learned. In this way, the user of Neural Networks (NN) is able to avoid the empirical testing of different structures. The method given here is applied to the recognition of different patterns derived from the EEG of an epileptic patient.
Image registration methods play a crucial role in computational neuroanatomy. This paper mainly contributes to the field of image registration with the use of nonlinear spatial transformations. Particularly, problems connected to matching magnetic resonance imaging (MRI) brain image data obtained from various subjects and with various imaging conditions are solved here. Registration is driven by local forces derived from multimodal point similarity measures which are estimated with the use of joint intensity histogram and tissue probability maps. A spatial deformation model imitating principles of continuum mechanics is used. Five similarity measures are tested in an experiment with image data obtained from the Simulated Brain Database and a quantitative evaluation of the algorithm is presented. Results of application of the method in automated spatial detection of anatomical abnormalities in first-episode schizophrenia are presented.
- MeSH
- algoritmy MeSH
- financování organizované MeSH
- interpretace obrazu počítačem metody MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- modely neurologické MeSH
- mozek anatomie a histologie fyziologie MeSH
- neuroanatomie metody MeSH
- neurologie metody MeSH
- počítačová simulace MeSH
- pružnost MeSH
- psychiatrie metody MeSH
- reprodukovatelnost výsledků MeSH
- rozpoznávání automatizované metody MeSH
- senzitivita a specificita MeSH
- subtrakční technika MeSH
- umělá inteligence MeSH
- vylepšení obrazu metody MeSH
- zobrazování trojrozměrné metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- hodnotící studie MeSH
Clinical islet transplantation programs rely on the capacities of individual centers to quantify isolated islets. Current computer-assisted methods require input from human operators. Here we describe two machine learning algorithms for islet quantification: the trainable islet algorithm (TIA) and the nontrainable purity algorithm (NPA). These algorithms automatically segment pancreatic islets and exocrine tissue on microscopic images in order to count individual islets and calculate islet volume and purity. References for islet counts and volumes were generated by the fully manual segmentation (FMS) method, which was validated against the internal DNA standard. References for islet purity were generated via the expert visual assessment (EVA) method, which was validated against the FMS method. The TIA is intended to automatically evaluate micrographs of isolated islets from future donors after being trained on micrographs from a limited number of past donors. Its training ability was first evaluated on 46 images from four donors. The pixel-to-pixel comparison, binary statistics, and islet DNA concentration indicated that the TIA was successfully trained, regardless of the color differences of the original images. Next, the TIA trained on the four donors was validated on an additional 36 images from nine independent donors. The TIA was fast (67 s/image), correlated very well with the FMS method (R2=1.00 and 0.92 for islet volume and islet count, respectively), and had small REs (0.06 and 0.07 for islet volume and islet count, respectively). Validation of the NPA against the EVA method using 70 images from 12 donors revealed that the NPA had a reasonable speed (69 s/image), had an acceptable RE (0.14), and correlated well with the EVA method (R2=0.88). Our results demonstrate that a fully automated analysis of clinical-grade micrographs of isolated pancreatic islets is feasible. The algorithms described herein will be freely available as a Fiji platform plugin.
- MeSH
- algoritmy MeSH
- automatizace MeSH
- krysa rodu rattus MeSH
- Langerhansovy ostrůvky * cytologie MeSH
- lidé MeSH
- počítačové zpracování obrazu * MeSH
- potkani Wistar MeSH
- strojové učení MeSH
- transplantace Langerhansových ostrůvků * MeSH
- zvířata MeSH
- Check Tag
- krysa rodu rattus MeSH
- lidé MeSH
- zvířata MeSH
