Quantifying and visualising uncertainty in deep learning-based segmentation for radiation therapy treatment planning: What do radiation oncologists and therapists want?

Background And Purpose: During the ESTRO 2023 physics workshop on "AI for the fully automated radiotherapy treatment chain", the topic of deep learning (DL) segmentation was discussed. Despite its widespread use in radiotherapy, the time needed to evaluate and correct DL segmentations remains burdensome. While segmentation uncertainty could be beneficial for clinicians, there is a lack of understanding on what information should be presented to ease their task.

Refining Treatment Planning in STereotactic Arrhythmia Radioablation: Benchmark Results and Consensus Statement From the STOPSTORM.eu Consortium.

Purpose: STereotactic Arrhythmia Radioablation (STAR) showed promising results in patients with refractory ventricular tachycardia. However, clinical data are scarce and heterogeneous. The STOPSTORM.