  Resective surgery may be curative for drug-resistant focal epilepsy, but only
40% to 70% of patients achieve seizure freedom after surgery. Retrospective
quantitative analysis could elucidate patterns in resected structures and
patient outcomes to improve resective surgery. However, the resection cavity
must first be segmented on the postoperative MR image. Convolutional neural
networks (CNNs) are the state-of-the-art image segmentation technique, but
require large amounts of annotated data for training. Annotation of medical
images is a time-consuming process requiring highly-trained raters, and often
suffering from high inter-rater variability. Self-supervised learning can be
used to generate training instances from unlabeled data. We developed an
algorithm to simulate resections on preoperative MR images. We curated a new
dataset, EPISURG, comprising 431 postoperative and 269 preoperative MR images
from 431 patients who underwent resective surgery. In addition to EPISURG, we
used three public datasets comprising 1813 preoperative MR images for training.
We trained a 3D CNN on artificially resected images created on the fly during
training, using images from 1) EPISURG, 2) public datasets and 3) both. To
evaluate trained models, we calculate Dice score (DSC) between model
segmentations and 200 manual annotations performed by three human raters. The
model trained on data with manual annotations obtained a median (interquartile
range) DSC of 65.3 (30.6). The DSC of our best-performing model, trained with
no manual annotations, is 81.7 (14.2). For comparison, inter-rater agreement
between human annotators was 84.0 (9.9). We demonstrate a training method for
CNNs using simulated resection cavities that can accurately segment real
resection cavities, without manual annotations.
