223 An Artificial Intelligence Framework for Brain Tumor Delineation in Intraoperative Ultrasound

Abstract

INTRODUCTION: Automated delineation of gliomas in intraoperative ultrasound (iUS) could provide timely information to surgeons. While artificial intelligence (AI) frameworks have shown promising results for delineating gliomas in preoperative MRI, automated delineation in iUS remains a challenging and open problem. METHODS: A deep learning algorithm was developed to automatically delineate gliomas in intraoperative ultrasound. Instead of using time-consuming manual delineations from experts, the algorithm was developed using MR-based automated delineations. First, a MR nnUnet was trained for delineating gliomas in 3D preoperative MRI using an annotated public dataset (N=1000; RSNA-ASNR-MICCAI-BraTS 2021). Then, this algorithm delineated gliomas in preoperative MRI of patients (N = 84) in whom pre-dural opening 3D iUS reconstructed from a tracked handheld 2D probe was also acquired. To obtain the MR-based delineations in iUS, preoperative MRI and iUS were aligned using NiftyReg. Finally, a 3D iUS nnUnet was trained using a random subset (N = 78) of this pseudo-annotated iUS dataset. The method was assessed on the remaining test set (N = 943 slices from 6 volumes), with a treating neurosurgeon delineating gliomas in 2D iUS only and then in 2D iUS and aligned MR slices to obtain gold-standard delineations. RESULTS: The median Dice scores of the nnUnet and the neurosurgeon were respectively 78.6% (IQ:15.5%) and 84.3% (IQ: 21.1%) against the gold-standard delineations. In this preliminary study, the network's performance was comparable to the neurosurgeon's (two paired one-sided tests, equivalence margin:5%, p<1e-5, N = 943), with rapid delineation (<10 second) of 3D iUS (95-216 slices) compared to neurogeons (32-93 minutes), making automated delineation practical during surgery. CONCLUSIONS: The results demonstrate the feasibility of using AI-based methods for automated delineation of brain tumors in iUS, which could improve surgical outcomes by providing real-time information to surgeons.