# A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections

arXiv: https://arxiv.org/abs/2105.11239 · HTML: https://mlsanity.com/paper/2105.11239/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 6 of 6)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning](https://mlsanity.com/paper/2003.04696/) (2020)
- [A Learning Strategy for Contrast-agnostic MRI Segmentation](https://mlsanity.com/paper/2003.01995/) (2020)
- [Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets](https://mlsanity.com/paper/2009.04009/) (2020)
- [Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy](https://mlsanity.com/paper/2007.03817/) (2020)
- [Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/2006.15693/) (2020)

## Built on by (most cited first, 7 of 7)

- [Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder](https://mlsanity.com/paper/2208.03203/) (2022)
- [Self-supervised Tumor Segmentation through Layer Decomposition](https://mlsanity.com/paper/2109.03230/) (2021)
- [Time-based Self-supervised Learning for Wireless Capsule Endoscopy](https://mlsanity.com/paper/2204.09773/) (2022)
- [Unsupervised Brain Tumor Segmentation with Image-based Prompts](https://mlsanity.com/paper/2304.01472/) (2023)
- [Three-dimensional end-to-end deep learning for brain MRI analysis](https://mlsanity.com/paper/2506.23916/) (2025)
- [Automatic and standardized surgical reporting for central nervous system tumors](https://mlsanity.com/paper/2508.08916/) (2025)
- [Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI](https://mlsanity.com/paper/2609.06807/) (2026)

## Abstract (arXiv)

Accurate 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. We 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. The 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). We 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/ressegijcars .