Challenges With Segmenting Intraoperative Ultrasound For Brain Tumours

Objective Addressing the challenges that come with identifying and delineating brain tumours in intraoperative ultrasound. Our goal is to both qualitatively and quantitatively assess the interobserver variation, amongst experienced neuro-oncological intraoperative ultrasound users (neurosurgeons and neuroradiologists), in detecting and segmenting brain tumours on ultrasound. We then propose that, due to the inherent challenges of this task, annotation by localisation of the entire tumour mass with a bounding box could serve as an ancillary solution to segmentation for clinical training, encompassing margin uncertainty and the curation of large datasets. Methods 30 ultrasound images of brain lesions in 30 patients were annotated by 4 annotators - 1 neuroradiologist and 3 neurosurgeons. The annotation variation of the 3 neurosurgeons was first measured, and then the annotations of each neurosurgeon were individually compared to the neuroradiologist’s, which served as a reference standard as their segmentations were further refined by cross-reference to the preoperative MRI. The following statistical metrics were used: Intersection Over Union, Sørensen-Dice similarity coefficient and Hausdorff distance. These annotations were then converted into bounding boxes for the same evaluation. Results There was a moderate level of interobserver variance between the neurosurgeons [IoU:0.789, DSC:0.876, HD:103.227] and a larger level of variance when compared against the MRI-informed reference standard annotations by the neuroradiologist, mean across annotators [IoU:0.723, DSC:0.813, HD:115.675]. After converting the segments to bounding boxes, all metrics improve, most significantly, the interquartile range drops by [IoU:37%, DSC:41%, HD:54%]. Conclusion This study highlights the current challenges with detecting and defining tumour boundaries in neuro-oncological intraoperative brain ultra-Sound. We then show that bounding box annotation could serve as a useful complementary approach for both clinical and technical reasons. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was supported by the UK Research and Innovation (UKRI) Centre for Doctoral Training in AI for Healthcare (EP/S023283/1), the Royal Society (URF\R\2 01014]), the NIHR Imperial Biomedical Research Centre, Canon Medical Systems and Brain Tumour Research (BTR) charity. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study had full local ethical approval by the HRA and Health and Care Research Wales (HCRW) authorities. Study title - US-CNS: Multiparametric Advanced Ultrasound Imaging of the Central Nervous System Intraoperatively and Through Gaps in the Bone, IRAS project ID: 275556, Protocol number: 22CX7609, REC reference: 22/WA/0259, Sponsor: Research Governance and Integrity Team (RGIT). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced will not be made available