PAHPhysRAD: A Digital Imaging and Communications in Medicine Research Tool for Segmentation and Radiomic Feature Extraction.

Introduction: Segmentation and analysis of organs at risks (OARs) and tumor volumes are integral concepts in the development of radiotherapy treatment plans and prediction of patients' treatment outcomes.

Aims: To develop a research tool, PAHPhysRAD, that can be used to semi- and fully automate segmentation of OARs. In addition, the proposed software seeks to extract 3214 radiomic features from tumor volumes and user-specified dose-volume parameters.

Repurposing traditional U-Net predictions for sparse SAM prompting in medical image segmentation.

Automated medical image segmentation (MIS) using deep learning has traditionally relied on models built and trained from scratch, or at least fine-tuned on a target dataset. The Segment Anything Model (SAM) by Meta challenges this paradigm by providing zero-shot generalisation capabilities. This study aims to develop and compare methods for refining traditional U-Net segmentations by repurposing them for automated SAM prompting.

Measuring herbicide (73.3 % glyphosate) exposure response in Halophila ovalis (previously johnsonii) and Halodule wrightii seagrass.

We evaluated the effects of Roundup QuikPRO™ (73.3 % glyphosate) using real-world herbicide application treatments: (1) overspray (low-dose), (2) powder spill (high-dose), and (3) controls (no-dose). Seagrass and water quality were monitored to observe responses to acute herbicide application.