Automated Measurement and Three-Dimensional Fitting of Corneal Ulcerations and Erosions via AI-Based Image Analysis.

PURPOSE: Artificial intelligence (AI)-tools hold great potential to compensate for missing resources in health-care systems but often fail to be implemented in clinical routine. Intriguingly, no-code and low-code technologies allow clinicians to develop Artificial intelligence (AI)-tools without requiring in-depth programming knowledge. Clinician-driven projects allow to adequately identify and address real clinical needs and, therefore, hold superior potential for clinical implementation. In this light, this study aimed for the clinician-driven development of a tool capable of measuring corneal lesions relative to total corneal surface area and eliminating inaccuracies in two-dimensional measurements by three-dimensional fitting of the corneal surface. METHODS: Standard slit-lamp photographs using a blue-light filter after fluorescein instillation taken during clinical routine were used to train a fully convolutional network to automatically detect the corneal white-to-white distance, the total fluorescent area and the total erosive area. Based on these values, the algorithm calculates the affected area relative to total corneal surface area and fits the area on a three-dimensional representation of the corneal surface. RESULTS: The developed algorithm reached dice scores >0.9 for an automated measurement of the relative lesion size. Furthermore, only 25% of conventional manual measurements were within a ± 10% range of the ground truth. CONCLUSIONS: The developed algorithm is capable of reliably providing exact values for corneal lesion sizes. Additionally, three-dimensional modeling of the corneal surface is essential for an accurate measurement of lesion sizes. Besides telemedicine applications, this approach harbors great potential for clinical trials where exact quantitative and observer-independent measurements are essential.

Evolutionary compromises in fungal fitness: hydrophobins can hinder the adverse dispersal of conidiospores and challenge their survival.

Fungal evolutionary biology is impeded by the scarcity of fossils, irregular life cycles, immortality, and frequent asexual reproduction. Simple and diminutive bodies of fungi develop inside a substrate and have exceptional metabolic and ecological plasticity, which hinders species delimitation. However, the unique fungal traits can shed light on evolutionary forces that shape the environmental adaptations of these taxa. Higher filamentous fungi that disperse through aerial spores produce amphiphilic and highly surface-active proteins called hydrophobins (HFBs), which coat spores and mediate environmental interactions. We exploited a library of HFB-deficient mutants for two cryptic species of mycoparasitic and saprotrophic fungi from the genus Trichoderma (Hypocreales) and estimated fungal development, reproductive potential, and stress resistance. HFB4 and HFB10 were found to be relevant for Trichoderma fitness because they could impact the spore-mediated dispersal processes and control other fitness traits. An analysis in silico revealed purifying selection for all cases except for HFB4 from T. harzianum, which evolved under strong positive selection pressure. Interestingly, the deletion of the hfb4 gene in T. harzianum considerably increased its fitness-related traits. Conversely, the deletion of hfb4 in T. guizhouense led to the characteristic phenotypes associated with relatively low fitness. The net contribution of the hfb4 gene to fitness was found to result from evolutionary tradeoffs between individual traits. Our analysis of HFB-dependent fitness traits has provided an evolutionary snapshot of the selective pressures and speciation process in closely related fungal species.

Sinoatrial Beat to Beat Variability Assessed by Contraction Strength in Addition to the Interbeat Interval.

Beat to beat variability of cardiac tissue or isolated cells is frequently investigated by determining time intervals from electrode measurements in order to compute scale dependent or scale independent parameters. In this study, we utilize high-speed video camera recordings to investigate the variability of intervals as well as mechanical contraction strengths and relative contraction strengths with nonlinear analyses. Additionally, the video setup allowed us simultaneous electrode registrations of extracellular potentials. Sinoatrial node tissue under control and acetylcholine treated conditions was used to perform variability analyses by computing sample entropies and Higuchi dimensions. Beat to beat interval variabilities measured by the two recording techniques correlated very well, and therefore, validated the video analyses for this purpose. Acetylcholine treatment induced a reduction of beating rate and contraction strength, but the impact on interval variability was negligible. Nevertheless, the variability analyses of contraction strengths revealed significant differences in sample entropies and Higuchi dimensions between control and acetylcholine treated tissue. Therefore, the proposed high-speed video camera technique might represent a non-invasive tool that allows long-lasting recordings for detecting variations in beating behavior over a large range of scales.

Pyramidal fractal dimension for high resolution images.

Fractal analysis (FA) should be able to yield reliable and fast results for high-resolution digital images to be applicable in fields that require immediate outcomes. Triggered by an efficient implementation of FA for binary images, we present three new approaches for fractal dimension (D) estimation of images that utilize image pyramids, namely, the pyramid triangular prism, the pyramid gradient, and the pyramid differences method (PTPM, PGM, PDM). We evaluated the performance of the three new and five standard techniques when applied to images with sizes up to 8192 × 8192 pixels. By using artificial fractal images created by three different generator models as ground truth, we determined the scale ranges with minimum deviations between estimation and theory. All pyramidal methods (PM) resulted in reasonable D values for images of all generator models. Especially, for images with sizes ≥1024×1024 pixels, the PMs are superior to the investigated standard approaches in terms of accuracy and computation time. A measure for the possibility to differentiate images with different intrinsic D values did show not only that the PMs are well suited for all investigated image sizes, and preferable to standard methods especially for larger images, but also that results of standard D estimation techniques are strongly influenced by the image size. Fastest results were obtained with the PDM and PGM, followed by the PTPM. In terms of absolute D values best performing standard methods were magnitudes slower than the PMs. Concluding, the new PMs yield high quality results in short computation times and are therefore eligible methods for fast FA of high-resolution images.

IQM: an extensible and portable open source application for image and signal analysis in Java.

Image and signal analysis applications are substantial in scientific research. Both open source and commercial packages provide a wide range of functions for image and signal analysis, which are sometimes supported very well by the communities in the corresponding fields. Commercial software packages have the major drawback of being expensive and having undisclosed source code, which hampers extending the functionality if there is no plugin interface or similar option available. However, both variants cannot cover all possible use cases and sometimes custom developments are unavoidable, requiring open source applications. In this paper we describe IQM, a completely free, portable and open source (GNU GPLv3) image and signal analysis application written in pure Java. IQM does not depend on any natively installed libraries and is therefore runnable out-of-the-box. Currently, a continuously growing repertoire of 50 image and 16 signal analysis algorithms is provided. The modular functional architecture based on the three-tier model is described along the most important functionality. Extensibility is achieved using operator plugins, and the development of more complex workflows is provided by a Groovy script interface to the JVM. We demonstrate IQM's image and signal processing capabilities in a proof-of-principle analysis and provide example implementations to illustrate the plugin framework and the scripting interface. IQM integrates with the popular ImageJ image processing software and is aiming at complementing functionality rather than competing with existing open source software. Machine learning can be integrated into more complex algorithms via the WEKA software package as well, enabling the development of transparent and robust methods for image and signal analysis.