Pascal Spiegler

Pascal Spiegler, Amirhossein Rasoulian, Yiming Xiao

Intracranial hemorrhage (ICH) is a life-threatening condition that requires rapid and accurate diagnosis to improve treatment outcomes and patient survival rates. Recent advancements in supervised deep learning have greatly improved the analysis of medical images, but often rely on extensive datasets with high-quality annotations, which are costly, time-consuming, and require medical expertise to prepare. To mitigate the need for large amounts of expert-prepared segmentation data, we have developed a novel weakly supervised ICH segmentation method that utilizes the YOLO object detection model and an uncertainty-rectified Segment Anything Model (SAM). In addition, we have proposed a novel point prompt generator for this model to further improve segmentation results with YOLO-predicted bounding box prompts. Our approach achieved a high accuracy of 0.933 and an AUC of 0.796 in ICH detection, along with a mean Dice score of 0.629 for ICH segmentation, outperforming existing weakly supervised and popular supervised (UNet and Swin-UNETR) approaches. Overall, the proposed method provides a robust and accurate alternative to the more commonly used supervised techniques for ICH quantification without requiring refined segmentation ground truths during model training.

Pascal Spiegler, Arash Harirpoush, Yiming Xiao

Crucial in disease analysis and surgical planning, manual segmentation of volumetric medical scans (e.g. MRI, CT) is laborious, error-prone, and challenging to master, while fully automatic algorithms can benefit from user feedback. Therefore, with the complementary power of the latest radiological AI foundation models and virtual reality (VR)'s intuitive data interaction, we propose SAMIRA, a novel conversational AI agent for medical VR that assists users with localizing, segmenting, and visualizing 3D medical concepts. Through speech-based interaction, the agent helps users understand radiological features, locate clinical targets, and generate segmentation masks that can be refined with just a few point prompts. The system also supports true-to-scale 3D visualization of segmented pathology to enhance patient-specific anatomical understanding. Furthermore, to determine the optimal interaction paradigm under near-far attention-switching for refining segmentation masks in an immersive, human-in-the-loop workflow, we compare VR controller pointing, head pointing, and eye tracking as input modes. With a user study, evaluations demonstrated a high usability score (SUS=90.0 $\pm$ 9.0), low overall task load, as well as strong support for the proposed VR system's guidance, training potential, and integration of AI in radiological segmentation tasks.