Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound.

We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a strategy, where intraoperative US images are synthesized from MR images accounting for multiple MR modalities and intraoperative US variability. We build our training set by enforcing keypoints localization over all images then train a patient-specific descriptor network that learns texture-invariant discriminant features in a supervised contrastive manner, leading to robust keypoints descriptors.

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.

Food Insufficiency Increased After The Expiration Of COVID-19 Emergency Allotments For SNAP Benefits In 2023.

In response to economic distress and food insecurity during the COVID-19 pandemic, Congress expanded the Supplemental Nutrition Assistance Program (SNAP) by introducing emergency allotments to increase monthly benefits, starting in March 2020. In March 2023, emergency allotments expired in the thirty-five states and territories still offering them. We provide some of the first evidence of the impacts of this loss of nutrition support-in some cases, more than $250 a month-for economically disadvantaged households.

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.