Purpose

Cancer in the head and neck area is commonly treated with radiotherapy. A key step for low-risk treatment is the accurate delineation of organs at risk in the planning imagery. The success of deep learning in image segmentation led to automated algorithms achieving human expert performance on certain datasets. However, such algorithms require large datasets for training and fail to segment previously unseen pathologies, where human experts still succeed. As pathologies are rare and large datasets costly to generate, we investigate the effect of: reduced training data, batch sizes and incorporation of prior knowledge.

Methods

The small data problem is studied by training a full-volume segmentation network with the reduced amount of data from the MICCAI 2015 head and neck segmentation challenge. To improve the segmentation, we evaluate the batch size as a hyper-parameter and first study and then incorporate a stacked autoencoder as shape prior into the training process.

Results

We found that using half of the training data (12 images of 25) results in an accuracy drop of only 3% for the segmentation of organs at risk. Also, the batch size turns out to be relevant for the quality of the segmentation when trained with less than half of the data. By applying PCA on the autoencoder’s latent space we achieve a compact and accurate shape model, which is used as a regularizer and significantly improves the segmentation results.

Conclusion

Small training data of up to 12 training images is enough to train accurate head and neck segmentation models. By using a shape prior for regularization, the performance of the segmentation can be improved significantly on the full dataset. When training on fewer than 12 images, the batch size is relevant and models have to be trained much longer until convergence.

中文翻译：

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在小数据集上训练具有先验形状的头颈部分割网络。

目的

头部和颈部区域的癌症通常通过放射疗法进行治疗。低风险治疗的一个关键步骤是在规划图像中准确描绘有风险的器官。深度学习在图像分割中的成功导致自动化算法在某些数据集上实现了人类专家的表现。然而，此类算法需要大型数据集进行训练，并且无法分割以前看不见的病理，而人类专家仍然可以成功。由于病理是罕见的且生成大型数据集的成本很高，我们研究了以下影响：减少训练数据、批量大小和先验知识的结合。

方法

通过使用来自 MICCAI 2015 头颈分割挑战的减少数据量训练全体积分割网络来研究小数据问题。为了改进分割，我们将批量大小作为超参数进行评估，首先进行研究，然后将堆叠的自动编码器作为先验形状纳入训练过程。

结果

我们发现，使用一半的训练数据（12 张图像，共 25 张）会导致风险器官分割的准确率仅下降 3%。此外，当使用少于一半的数据进行训练时，批次大小与分割质量相关。通过在自编码器的潜在空间上应用 PCA，我们实现了紧凑而准确的形状模型，用作正则化器并显着改善了分割结果。

结论

多达 12 张训练图像的小训练数据足以训练准确的头颈部分割模型。通过使用先验形状进行正则化，可以在完整数据集上显着提高分割性能。当训练少于 12 张图像时，批量大小是相关的，并且模型必须训练更长时间直到收敛。