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.

Development and Characterization of a Novel Neuropilin-2 Antibody for Immunohistochemical Staining of Cancer and Sarcoidosis Tissue Samples.

Neuropilin-2 (NRP2) is a cell surface receptor that plays key roles in lymphangiogenesis, but also in pathophysiological conditions such as cancer and inflammation. NRP2 targeting by efzofitimod, a novel immunomodulatory molecule, is currently being tested for the treatment of pulmonary sarcoidosis. To date, no anti-NRP2 antibodies are available for companion diagnostics. Here we describe the development and characterization of a novel NRP2 antibody. Using a variety of research techniques, that is, enzyme-linked immunoassay, Western blot, biolayer interferometry, and immunohistochemistry, we demonstrate that our antibody detects all major NRP2 isoforms and does not cross-react with NRP1. Using this antibody, we show high NRP2 expression in granulomas from sarcoidosis patient skin and lung biopsies. Our novel anti-NRP2 antibody could prove to be a useful clinical tool for sarcoidosis and other indications where NRP2 has been implicated. Clinical Trial Registration: clinicaltrials.gov NCT05415137.

Deep Learning-Based Image Analysis for the Quantification of Tumor-Induced Angiogenesis in the 3D In Vivo Tumor Model-Establishment and Addition to Laser Speckle Contrast Imaging (LSCI).

(1) Background: angiogenesis plays an important role in the growth and metastasis of tumors. We established the CAM assay application, an image analysis software of the IKOSA platform by KML Vision, for the quantification of blood vessels with the in ovo chorioallantoic membrane (CAM) model. We added this proprietary deep learning algorithm to the already established laser speckle contrast imaging (LSCI). (2) Methods: angiosarcoma cell line tumors were grafted onto the CAM. Angiogenesis was measured at the beginning and at the end of tumor growth with both measurement methods. The CAM assay application was trained to enable the recognition of in ovo CAM vessels. Histological stains of the tissue were performed and gluconate, an anti-angiogenic substance, was applied to the tumors. (3) Results: the angiosarcoma cells formed tumors on the CAM that appeared to stay vital and proliferated. An increase in perfusion was observed using both methods. The CAM assay application was successfully established in the in ovo CAM model and anti-angiogenic effects of gluconate were observed. (4) Conclusions: the CAM assay application appears to be a useful method for the quantification of angiogenesis in the CAM model and gluconate could be a potential treatment of angiosarcomas. Both aspects should be evaluated in further research.

A 3D In Vivo Model for Studying Human Renal Cystic Tissue and Mouse Kidney Slices.

(1) Background: Autosomal dominant polycystic kidney disease (ADPKD) is a frequent monogenic disorder that leads to progressive renal cyst growth and renal failure. Strategies to inhibit cyst growth in non-human cyst models have often failed in clinical trials. There is a significant need for models that enable studies of human cyst growth and drug trials. (2) Methods: Renal tissue from ADPKD patients who received a nephrectomy as well as adult mouse kidney slices were cultured on a chorioallantoic membrane (CAM) for one week. The cyst volume was monitored by microscopic and CT-based applications. The weight and angiogenesis were quantified. Morphometric and histological analyses were performed after the removal of the tissues from the CAM. (3) Results: The mouse and human renal tissue mostly remained vital for about one week on the CAM. The growth of cystic tissue was evaluated using microscopic and CT-based volume measurements, which correlated with weight and an increase in angiogenesis, and was accompanied by cyst cell proliferation. (4) Conclusions: The CAM model might bridge the gap between animal studies and clinical trials of human cyst growth, and provide a drug-testing platform for the inhibition of cyst enlargement. Real-time analyses of mouse kidney tissue may provide insights into renal physiology and reduce the need for animal experiments.

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.

Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization.

Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer, which was developed to participate in the GlaS@MICCAI2015 colon gland segmentation challenge. We use two distinct deep convolutional neural networks (CNN) for pixel-wise classification of Hematoxylin-Eosin stained images. While the first classifier separates glands from background, the second classifier identifies gland-separating structures. In a subsequent step, a figure-ground segmentation based on weighted total variation produces the final segmentation result by regularizing the CNN predictions. We present both quantitative and qualitative segmentation results on the recently released and publicly available Warwick-QU colon adenocarcinoma dataset associated with the GlaS@MICCAI2015 challenge and compare our approach to the simultaneously developed other approaches that participated in the same challenge. On two test sets, we demonstrate our segmentation performance and show that we achieve a tissue classification accuracy of 98% and 95%, making use of the inherent capability of our system to distinguish between benign and malignant tissue. Our results show that deep learning approaches can yield highly accurate and reproducible results for biomedical image analysis, with the potential to significantly improve the quality and speed of medical diagnoses.

Training echo state networks for rotation-invariant bone marrow cell classification.

The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.

Gland segmentation in colon histology images: The glas challenge contest.

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

Collaborative analysis of multi-gigapixel imaging data using Cytomine.

MOTIVATION: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. RESULTS: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications. AVAILABILITY AND IMPLEMENTATION: Cytomine (http://www.cytomine.be/) is freely available under an open-source license from http://github.com/cytomine/ A documentation wiki (http://doc.cytomine.be) and a demo server (http://demo.cytomine.be) are also available. CONTACT: info@cytomine.be SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Subcutaneous fat patterning in athletes: selection of appropriate sites and standardisation of a novel ultrasound measurement technique: ad hoc working group on body composition, health and performance, under the auspices of the IOC Medical Commission.

BACKGROUND: Precise and accurate field methods for body composition analyses in athletes are needed urgently. AIM: Standardisation of a novel ultrasound (US) technique for accurate and reliable measurement of subcutaneous adipose tissue (SAT). METHODS: Three observers captured US images of uncompressed SAT in 12 athletes and applied a semiautomatic evaluation algorithm for multiple SAT measurements. RESULTS: Eight new sites are recommended: upper abdomen, lower abdomen, erector spinae, distal triceps, brachioradialis, lateral thigh, front thigh, medial calf. Obtainable accuracy was 0.2 mm (18 MHz probe; speed of sound: 1450 m/s). Reliability of SAT thickness sums (N=36): R(2)=0.998, SEE=0.55 mm, ICC (95% CI) 0.998 (0.994 to 0.999); observer differences from their mean: 95% of the SAT thickness sums were within ± 1 mm (sums of SAT thicknesses ranged from 10 to 50 mm). Embedded fibrous tissues were also measured. CONCLUSIONS: A minimum of eight sites is suggested to accommodate inter-individual differences in SAT patterning. All sites overlie muscle with a clearly visible fascia, which eases the acquisition of clear images and the marking of these sites takes only a few minutes. This US method reaches the fundamental accuracy and precision limits for SAT measurements given by tissue plasticity and furrowed borders, provided the measurers are trained appropriately.

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.

Body composition in sport: a comparison of a novel ultrasound imaging technique to measure subcutaneous fat tissue compared with skinfold measurement.

BACKGROUND: Extremely low weight and rapid changes in weight and body composition have become major concerns in many sports, but sufficiently accurate field methods for body composition assessment in athletes are missing. This study aimed to explore the use of ultrasound methods for assessment of body fat content in athletes. METHODS: 19 female athletes (stature: 1.67(± 0.06) m, weight: 59.6(± 7.6) kg; age: 19.5(± 3.3) years) were investigated by three observers using a novel ultrasound method for thickness measurement of uncompressed subcutaneous adipose tissue and of embedded structures. Two observers also measured skinfold thickness at eight International Society for the Advancement of Kinanthrometry (ISAK) sites; mean skinfold values were compared to mean subcutaneous adipose tissue thicknesses measured by ultrasound. Interobserver reliability of imaging and evaluation obtained by this ultrasound technique: intraclass correlation coefficient ICC=0.968 (95% CI 0.957 to 0.977); evaluation of given images: ICC=0.997 (0.993 to 0.999). RESULTS: Skinfold compared to ultrasound thickness showed that compressibility of subcutaneous adipose tissue depends largely on the site and the person: regression slopes ranged from 0.61 (biceps) to 1.59 (thigh) and CIs were large. Limits of agreement ranged from 2.6 to 8.6 mm. Regression lines did not intercept the skinfold axis at zero because of the skin thickness being included in the skinfold. The four ISAK trunk sites caused ultrasound imaging problems in 13 of 152 sites (8 ISAK sites, 19 athletes). CONCLUSIONS: The ultrasound method allows measurement of uncompressed subcutaneous adipose tissue thickness with an accuracy of 0.1-0.5 mm, depending on the probe frequency. Compressibility of the skinfold depends on the anatomical site, and skin thickness varies by a factor of two. This inevitably limits the skinfold methods for body fat estimation. Ultrasound accuracy for subcutaneous adipose tissue measurement is limited by the plasticity of fat and furrowed tissue borders. Comparative US measurements show that skinfold measurements do not allow accurate assessment of subcutaneous adipose tissue thickness.

Body composition in sport: interobserver reliability of a novel ultrasound measure of subcutaneous fat tissue.

BACKGROUND: Very low body mass, extreme mass changes, and extremely low per cent body fat are becoming increasingly common in many sports, but sufficiently reliable and accurate field methods for body composition assessment in athletes are missing. METHODS: Nineteen female athletes were investigated (mean (SD) age: 19.5 (± 3.3) years; body mass: 59.6 (± 7.6) kg; height: 1.674 (± 0.056) m; BMI: 21.3 (± 2.3) kg/m(2)). Three observers applied diagnostic B-mode-ultrasound (US) combined with the evaluation software for subcutaneous adipose tissue measurements at eight ISAK sites (International Society for the Advancement of Kinanthrometry). Regression and reliability analyses are presented. RESULTS: US measurements and evaluation of subcutaneous adipose tissue (SAT) thicknesses (including fibrous structures: D(included); n=378) resulted in an SE of estimate SEE=0.60 mm, R(2)=0.98 (p<0.001), limit of agreement LOA=1.18, ICC=0.968 (0.957-0.977). Similar values were found for D(excluded): SEE=0.68 mm, R(2)=0.97 (p<0.001). D(included) at individual ISAK sites: at biceps, R(2)=0.87 and intraclass-correlation coefficient ICC=0.811 were lowest and SEE=0.79 mm was highest. Values at all other sites ranged from R(2): 0.94-0.99, SEE: 0.42-0.65 mm, and ICC: 0.917-0.985. Interobserver coefficients ranged from 0.92 to 0.99, except for biceps (0.74, 0.83 and 0.87). Evaluations of 20 randomly selected US images by three observers (D(included)) resulted in: SEE=0.15 mm, R(2)=0.998(p<0.001), ICC=0.997 (0.993, 0999). CONCLUSIONS: Subject to optimal choice of sites and certain standardisations, US can offer a highly reliable field method for measurement of uncompressed thickness of the SAT. High accuracy and high reliability of measurement, as obtained with this US approach, are essential for protection of the athlete's health and also for optimising performance.