Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking.

The high rate of drug withdrawal from the market due to cardiovascular toxicity or lack of efficacy, the economic burden, and extremely long time before a compound reaches the market, have increased the relevance of human in vitro models like human (patient-derived) pluripotent stem cell (hPSC)-derived engineered heart tissues (EHTs) for the evaluation of the efficacy and toxicity of compounds at the early phase in the drug development pipeline. Consequently, the EHT contractile properties are highly relevant parameters for the analysis of cardiotoxicity, disease phenotype, and longitudinal measurements of cardiac function over time. In this study, we developed and validated the software HAARTA (Highly Accurate, Automatic and Robust Tracking Algorithm), which automatically analyzes contractile properties of EHTs by segmenting and tracking brightfield videos, using deep learning and template matching with sub-pixel precision. We demonstrate the robustness, accuracy, and computational efficiency of the software by comparing it to the state-of-the-art method (MUSCLEMOTION), and by testing it with a data set of EHTs from three different hPSC lines. HAARTA will facilitate standardized analysis of contractile properties of EHTs, which will be beneficial for in vitro drug screening and longitudinal measurements of cardiac function.

Vision-Based Module for Herding with a Sheepdog Robot.

Livestock farming is assisted more and more by technological solutions, such as robots. One of the main problems for shepherds is the control and care of livestock in areas difficult to access where grazing animals are attacked by predators such as the Iberian wolf in the northwest of the Iberian Peninsula. In this paper, we propose a system to automatically generate benchmarks of animal images of different species from iNaturalist API, which is coupled with a vision-based module that allows us to automatically detect predators and distinguish them from other animals. We tested multiple existing object detection models to determine the best one in terms of efficiency and speed, as it is conceived for real-time environments. YOLOv5m achieves the best performance as it can process 64 FPS, achieving an mAP (with IoU of 50%) of 99.49% for a dataset where wolves (predator) or dogs (prey) have to be detected and distinguished. This result meets the requirements of pasture-based livestock farms.

Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures.

Delineation of curvilinear structures in images is an important basic step of several image processing applications, such as segmentation of roads or rivers in aerial images, vessels or staining membranes in medical images, and cracks in pavements and roads, among others. Existing methods suffer from insufficient robustness to noise. In this paper, we propose a novel operator for the detection of curvilinear structures in images, which we demonstrate to be robust to various types of noise and effective in several applications. We call it RUSTICO, which stands for RobUST Inhibition-augmented Curvilinear Operator. It is inspired by the push-pull inhibition in visual cortex and takes as input the responses of two trainable B-COSFIRE filters of opposite polarity. The output of RUSTICO consists of a magnitude map and an orientation map. We carried out experiments on a data set of synthetic stimuli with noise drawn from different distributions, as well as on several benchmark data sets of retinal fundus images, crack pavements, and aerial images and a new data set of rose bushes used for automatic gardening. We evaluated the performance of RUSTICO by a metric that considers the structural properties of line networks (connectivity, area, and length) and demonstrated that RUSTICO outperforms many existing methods with high statistical significance. RUSTICO exhibits high robustness to noise and texture.

Trainable COSFIRE filters for vessel delineation with application to retinal images.

Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se=0.7655, Sp=0.9704; STARE: Se=0.7716, Sp=0.9701; CHASE_DB1: Se=0.7585, Sp=0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.