An effective and open source interactive 3D medical image segmentation solution.

3D medical image segmentation is a key step in numerous clinical applications. Even though many automatic segmentation solutions have been proposed, it is arguably that medical image segmentation is more of a preference than a reference as inter- and intra-variability are widely observed in final segmentation output. Therefore, designing a user oriented and open-source solution for interactive annotation is of great value for the community.

An open annotated dataset and baseline machine learning model for segmentation of vertebrae with metastatic bone lesions from CT.

Automatic analysis of pathologic vertebrae from computed tomography (CT) scans could significantly improve the diagnostic management of patients with metastatic spine disease. We provide the first publicly available annotated imaging dataset of cancerous CT spines to help develop artificial intelligence frameworks for automatic vertebrae segmentation and classification. This collection contains a dataset of 55 CT scans collected on patients with various types of primary cancers at two different institutions.

Registration of Longitudinal Spine CTs for Monitoring Lesion Growth.

Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration is crucial for clinical use, however, it is challenging due to substantial change in shape and appearance due to lesions. In this paper we present a novel method to automatically align longitudinal spine CTs and accurately assess lesion progression.

TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography.

Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely , that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography.

A systems-based analysis to rethink the European environmental risk assessment of regulated chemicals using pesticides as a pilot case.

A growing body of scientific literature stresses the need to advance current environmental risk assessment (ERA) methodologies and associated regulatory frameworks to better address the landscape-scale and long-term impact of pesticide use on biodiversity and the ecosystem. Moreover, more collaborative and integrative approaches are needed to meet sustainability goals. The One Health approach is increasingly applied by the European Food Safety Authority (EFSA) to support the transition towards safer, healthier and more sustainable food.