Publisher Correction: Image Data Resource: a bioimage data integration and publication platform.

This paper was originally published under standard Nature America Inc. copyright. As of the date of this correction, the Resource is available online as an open-access paper with a CC-BY license. No other part of the paper has been changed.

The Image Data Resource: A Bioimage Data Integration and Publication Platform.

Access to primary research data is vital for the advancement of science. To extend the data types supported by community repositories, we built a prototype Image Data Resource (IDR) that collects and integrates imaging data acquired across many different imaging modalities. IDR links data from several imaging modalities, including high-content screening, super-resolution and time-lapse microscopy, digital pathology, public genetic or chemical databases, and cell and tissue phenotypes expressed using controlled ontologies. Using this integration, IDR facilitates the analysis of gene networks and reveals functional interactions that are inaccessible to individual studies. To enable re-analysis, we also established a computational resource based on Jupyter notebooks that allows remote access to the entire IDR. IDR is also an open source platform that others can use to publish their own image data. Thus IDR provides both a novel on-line resource and a software infrastructure that promotes and extends publication and re-analysis of scientific image data.

Cost-effectiveness of genetic testing of endocrine tumor patients using a comprehensive hereditary cancer gene panel.

INTRODUCTION: Heterogenous clinical manifestations, overlapping phenotypes and complex genetic backgrounds are common in patients with endocrine tumors. There are no comprehensive recommendations for genetic testing and counselling of these patients compared to other hereditary cancer syndromes. The application of multigene panel testing is common in clinical genetic laboratories, but their performance for patients with endocrine tumors has not been assessed. METHODS: As a national reference center, we prospectively tested the diagnostic utility and cost-efficiency of a multigene panel covering 113 genes representing genetic susceptibility for solid tumors. 1279 patients (including 96 cases with endocrine tumors) were evaluated between October 2021 and December 2022 who were suspected to have hereditary tumor syndromes. RESULTS: The analytical performance of the hereditary cancer panel was suitable for diagnostic testing. Clinical diagnosis was confirmed in 24% (23/96); incidental findings in genes not associated with the patient's phenotype were identified in 5% (5/96). A further 7% of pathogenic/likely pathogenic variants were detected in genes with potential genetic susceptibility roles but currently no clear clinical consequence. Cost-benefit analysis showed that the application of a more comprehensive gene panel in a diagnostic laboratory yielded a shorter turnaround time and provided additional genetic results with the same cost and workload. DISCUSSION: Using comprehensive multigene panel results in faster turnaround time and cost-efficiently identifies genetic alterations in hereditary endocrine tumor syndromes. Incidentally identified variants in patients with poor prognoses may serve as a potential therapeutic target in tumors where therapeutic possibilities are limited.

Mineotaur: a tool for high-content microscopy screen sharing and visual analytics.

High-throughput/high-content microscopy-based screens are powerful tools for functional genomics, yielding intracellular information down to the level of single-cells for thousands of genotypic conditions. However, accessing their data requires specialized knowledge and most often that data is no longer analyzed after initial publication. We describe Mineotaur ( http://www.mineotaur.org ), a open-source, downloadable web application that allows easy online sharing and interactive visualisation of large screen datasets, facilitating their dissemination and further analysis, and enhancing their impact.

Improving microaneurysm detection in color fundus images by using context-aware approaches.

In this paper, we present two approaches to improve microaneurysm detector ensembles. First, we provide an approach to select a set of preprocessing methods for a microaneurysm candidate extractor to enhance its detection performance in color fundus images. The performance of the candidate extractor with each preprocessing method is measured in six microaneurysm categories. The best performing preprocessing method for each category is selected and organized into an ensemble-based method. We tested our approach on the publicly available DiaretDB1 database, where the proposed approach led to an improvement regarding the individual approaches. Second, an adaptive weighting approach for microaneurysm detector ensembles is presented.The basis of the adaptive weighting approach is the spatial location and contrast of the detected microaneurysm. During training, the performance of ensemble members is measured with respect to these contextual information, which serves as a basis for the optimal weights assigned to the detectors. We have tested this approach on two publicly available datasets, where it showed its competitiveness compared without previously published ensemble-based approach for microaneurysm detection. Moreover, the proposed approach outperformed all the investigated individual detectors.

An ensemble-based system for microaneurysm detection and diabetic retinopathy grading.

Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first, and also on two other databases. Since microaneurysm detection is decisive in diabetic retinopathy (DR) grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90 ± 0.01 is achieved in a "DR/non-DR"-type classification based on the presence or absence of the microaneurysms.

An adaptive weighting approach for ensemble-based detection of microaneurysms in color fundus images.

In this paper, we present an adaptive weighting approach to microaneurysm detector ensembles. The basis of the adaptive weighting approach is the spatial location and contrast of the detected microaneurysm. During training, the performance of ensemble members is measured with a respect to these contextual information, which serves as a basis for the optimal weights assigned to detectors. We have tested this approach on two publicly available datasets, where it showed its competitiveness compared with out previously published ensemble-based approach for microaneurysm detection. Moreover, the proposed approach outperformed all the investigated individual detectors.

Evaluation of the grading performance of an ensemble-based microaneurysm detector.

In this paper, results of a diabetic retinopathy screening experiment are presented which is based solely on the findings of a microaneurysm detector. For this purpose, an ensemble-based algorithm developed by our research group was used; this provided promising results in our earlier experiments. At its best, the 1200 image of the Messidor database is classified by this detector with a sensitivity of 96%, a specificity of 51% and achieved an AUC of 0.87. As anticipated, larger microaneurysm counts are recognized with higher level of certainty. Therefore, this approach might be expected to have good performance in relation to the severity of the disease.