Cellular spheroid is a complex aggregate of cells, and imaging is an important step in understanding how cell behavior operates and give references about possibilities according to the cells in the spheroid formation. Identifying structures in these images is manual and time-consuming and have a high rate of variability inter experts. An automated identification can solve these problems. The aim of this work is to present a study of Convolutional Neural Network (CNN) applied to live cells identification in NIH-3T3 spheroid. Four different CNN architectures are exploited in this paper: AlexNet, ResNet18, GoogLeNet, and VGG 16 with batch normalization. Many experiments were performed to get the best architecture involving data augmentation, hyperparameter tuning, and transfer learning using ImageNet. The experiments identify up to five different structures in a spheroid image, where the AlexNet achieved the best performance considering the F1-score as the evaluation metric. The use of CNN for this kind of identification opens the possibility of following the spheroid’s behaviour when cultured in more complex images.
One of the main challenges in the integration of medical data reports is translating numerical features from different sources into a common abstract vocabulary that support a seamless combination of such data. When it comes to image analysis, a very common pipeline to describe the image involves extracting numerical features from image data and translate them into meaningful pre-defined semantic concepts. In this context, we propose a methodology for selecting numerical features and relating them to semantic features using the publicly available categorization in the lung nodules LIDC NIH database. We present several numerical features joined several classifiers, and a comparison between two feature selection methods and discuss how different features contribute to the discrimination of different semantic characteristics of lung nodules. Our results show the potential of such methodology for translating features into abstract semantic concepts for lung nodules characterization.
Intravascular optical coherence tomography (IOCT) is a modality that provides sufficient resolution for very accurate visualization of localized cardiovascular conditions, such as coronary artery calcification (CAC). CAC quantification in IOCT images is still performed mostly manually, which is time consuming, considering that each IOCT exam has more than two hundred 2D slices. An automated method for CAC detection in IOCT would add valuable information for clinicians when treating patients with coronary atherosclerosis. In this context, we propose an approach that uses a fully connected neural network (FCNN) for CAC detection in IOCT images using a small training dataset. In our approach, we transform the input image to polar coordinate transformation using as reference the centroid from the lumen segmentation, that restricts the variability in CAC spatial position, which we proved to be beneficial for the CNN training with few training data. We analyzed 51 slices from in-vivo human coronaries and the method achieved 63.6% sensitivity and 99.8% specificity for segmenting CAC. Our results demonstrate that it is possible to successfully detect and segment calcific plaques in IOCT images using FCNNs.
Studies in intravascular optical coherence tomography (IV-OCT) have demonstrated the importance of coronary bifurcation regions in intravascular medical imaging analysis, as plaques are more likely to accumulate in this region leading to coronary disease. A typical IV-OCT pullback acquires hundreds of frames, thus developing an automated tool to classify the OCT frames as bifurcation or non-bifurcation can be an important step to speed up OCT pullbacks analysis and assist automated methods for atherosclerotic plaque quantification. In this work, we evaluate the performance of two state-of-the-art classifiers, SVM and Neural Networks in the bifurcation classification task. The study included IV-OCT frames from 9 patients. In order to improve classification performance, we trained and tested the SVM with different parameters by means of a grid search and different stop criteria were applied to the Neural Network classifier: mean square error, early stop and regularization. Different sets of features were tested, using feature selection techniques: PCA, LDA and scalar feature selection with correlation. Training and test were performed in sets with a maximum of 1460 OCT frames. We quantified our results in terms of false positive rate, true positive rate, accuracy, specificity, precision, false alarm, f-measure and area under ROC curve. Neural networks obtained the best classification accuracy, 98.83%, overcoming the results found in literature. Our methods appear to offer a robust and reliable automated classification of OCT frames that might assist physicians indicating potential frames to analyze. Methods for improving neural networks generalization have increased the classification performance.
Vessel branchings are critical vascular locations from the clinical point of view. In these sites, the arterial hemodynamic plays a relevant role in the progression of atherosclerosis, an important vascular pathology. In this paper, a fully automatic approach for the bifurcation classification in human Intravascular Optical Coherence Tomography (IV-OCT) sequences is introduced. Given the lumen contours, the method is capable of labeling the bifurcation slices. A geometric feature extraction was performed and the Forward Regression Orthogonal Least Squares method (FROLS) was applied to analyze the best features and to determine the appropriated weights in a binary classifier. A cross-validation scheme is applied in order to evaluate the performance of the classification approach and the results have shown a sensitivity of 86% and specificity of 92% to FROLS.
The analysis of vascular structure based on vessel diameters, density and distance between bifurcations is an important
step towards the diagnosis of vascular anomalies. Moreover, vascular network extraction allows the study of angiogenesis. This work describes a technique that detects bifurcations in vascular networks in magnetic resonance angiography and computed tomography angiography images. Initially, a vessel tracking technique that uses the Hough transform and a matrix composed of second order partial derivatives of image intensity is used to estimate the scale and vessel direction, respectively. This semi-automatic technique is capable of connecting isolated tracked vessel segments and extracting a full tree from a vascular network with minimal user intervention. Vessel shape descriptors such as curvature are then used to identify bifurcations during tracking and to estimate the next branch direction. We have initially applied this technique on synthetic datasets and then on real images.
Segmentation of blood vessels from magnetic resonance angiography (MRA) or computed tomography angiography (CTA) images is a complex process that usually takes a lot of computational resources. Also, most vascular
segmentation and detection algorithms do not work properly due to the wide architectural variability of the
blood vessels. Thus, the construction of convincing synthetic vascular trees makes it possible to validate new
segmentation methodologies. In this work, an extension to the traditional Lindenmayer system (L-system) that
generates synthetic 3D blood vessels by adding stochastic rules and parameters to the grammar is proposed. Towards this aim, we implement a parser and a generator of L-systems whose grammars simulate natural features
of real vessels such as the bifurcation angle, average length and diameter, as well as vascular anomalies, such as
aneurysms and stenoses. The resulting expressions are then used to create synthetic vessel images that mimic
MRA and CTA images. In addition, this methodology allows for vessel growth to be limited by arbitrary 3D
surfaces, and the vessel intensity profille can be tailored to match real angiographic intensities.
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