Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A self-supervised learning strategy for postoperative brain cavity
  segmentation simulating resections   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 .

15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph