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 .
翻译:288. 我们用模拟法对3DCNN进行自我监督的机械化分析。我们用模拟法对3DCNN的RC解析进行了自我监督培训。我们用430个后期手术和268个前期呼吸机的数据集(由430个复发性心血管外科病人组成的数据集)。我们用高度训练的收费器,并可能受到高度的跨行变异性。自我监督的学习战略可以利用未贴标签的数据来进行培训。我们开发了一种算法,以模拟术前磁共振动图像的重新解析。我们用模拟法对3DCNN进行了自我监督的3DNCNN的RC解析技术培训。我们用430个后期手术和268个前期呼吸机。我们用三个小节后数据集和EPISURG的附加图象(由20、33、19和133个科目组成)。