ArticleActa neurochirurgica2024
Challenges with segmenting intraoperative ultrasound for brain tumours.
Article in Acta neurochirurgica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of the hybrid machine learning models for brain tumour segmentation and detection in medical images.Frontiers in artificial intelligence · 2025Pooled it
- U-ConvNext: A Robust Approach to Glioma Segmentation in Intraoperative Ultrasound.Journal of imaging informatics in medicine · 2026Article
- Beyond segmentation accuracy: understanding model reliability and failure modes in ultrasound image analysis.Frontiers in medical technology · 2026Article
- Automatic Brain Tumor Segmentation in 2D Intra-Operative Ultrasound Images Using Magnetic Resonance Imaging Tumor Annotations.Journal of imaging · 2025Article
- Automated Brain Tumor MRI Segmentation Using ARU-Net with Residual-Attention Modules.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- A method for mimicking tumour tissue in brain ex-vivo ultrasound for research application and clinical training.Acta neurochirurgica · 2025Article
- Inter-observer agreement in interpreting intraoperative ultrasonography during brain tumour surgery.Frontiers in surgery · 2025Article
- Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
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 magnetic resonance imaging (MRI). The following statistical metrics were used: Intersection Over Union (IoU), Sørensen-Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). These annotations were then converted into bounding boxes for the same evaluation. Results - There was a moderate level of interobserver variance between the neurosurgeons
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