ArticleInternational journal of computer assisted radiology and surgery2021
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections.
Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 27 citations in OpenAlex.
- Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI.Research square · 2026Article
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- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- AI enabled decision support systems in epilepsy surgery a scoping review.Research square · 2026Article
- Deep learning enabled decision support systems in epilepsy surgery: a scoping review.npj health systems · 2026Article
- Optimizing registration uncertainty visualization to support intraoperative decision-making during brain tumor resection.International journal of computer assisted radiology and surgery · 2025Article
- The impact of resective epilepsy surgery on the brain network: evidence from post-surgical imaging.Brain : a journal of neurology · 2025Review
- From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping.Bio-protocol · 2025Article
- Long-term memory plasticity in a decade-long connectivity study post anterior temporal lobe resection.Nature communications · 2025Article
- Automatic and standardized reporting of perioperative MRIs in patients with central nervous system tumors.Frontiers in neurology · 2025Article
- Increased functional network segregation in glioma patients posttherapy: A neurological compensatory response or catastrophe for cognition?Network neuroscience (Cambridge, Mass.) · 2025Article
- Automated generation of epilepsy surgery resection masks: The RAMPS pipeline.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Brain network topology and its cognitive impact in adult glioma survivors.Scientific reports · 2024Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- Extent of piriform cortex resection in children with temporal lobe epilepsy.Annals of clinical and translational neurology · 2023Article
- Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder.Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop) · 2022Article
- The relationship between radiotherapy dose and cognitive outcomes in functional brain network hubs of glioma patients with predominantly left frontal lobe lesions.Neuro-oncology advancesArticle
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Authors and funding
12 authors at 6 institutions in 3 countries.
Funding
Abstract
purposeAccurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional neural networks (CNNs) are the state-of-the-art image segmentation technique, but require large annotated datasets for training. Annotation of 3D medical images is time-consuming, requires highly trained raters and may suffer from high inter-rater variability. Self-supervised learning strategies can leverage unlabeled data for training.
methodsWe developed an algorithm to simulate resections from preoperative magnetic resonance images (MRIs). We performed self-supervised training of a 3D CNN for RC segmentation using our simulation method. We curated EPISURG, a dataset comprising 430 postoperative and 268 preoperative MRIs from 430 refractory epilepsy patients who underwent resective neurosurgery. We fine-tuned our model on three small annotated datasets from different institutions and on the annotated images in EPISURG, comprising 20, 33, 19 and 133 subjects.
resultsThe model trained on data with simulated resections obtained median (interquartile range) Dice score coefficients (DSCs) of 81.7 (16.4), 82.4 (36.4), 74.9 (24.2) and 80.5 (18.7) for each of the four datasets. After fine-tuning, DSCs were 89.2 (13.3), 84.1 (19.8), 80.2 (20.1) and 85.2 (10.8). For comparison, inter-rater agreement between human annotators from our previous study was 84.0 (9.9).
conclusionWe present a self-supervised learning strategy for 3D CNNs using simulated RCs to accurately segment real RCs on postoperative MRI. Our method generalizes well to data from different institutions, pathologies and modalities. Source code, segmentation models and the EPISURG dataset are available at https://github.com/fepegar/resseg-ijcars .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.