Abstract
Whole brain parcellation requires inferring hundreds of segmentation labels in large image volumes and thus presents significant practical challenges for deep learning approaches. We introduce label merge-and-split, a method that first greatly reduces the effective number of labels required for learning-based whole brain parcellation and then recovers original labels. Using a greedy graph colouring algorithm, our method automatically groups and merges multiple spatially separate labels prior to model training and inference. The merged labels may be semantically unrelated. A deep learning model is trained to predict merged labels. At inference time, original labels are restored using atlas-based influence regions. In our experiments, the proposed approach reduces the number of labels by up to 68% while achieving segmentation accuracy comparable to the baseline method without label merging and splitting. Moreover, model training and inference times as well as GPU memory requirements were reduced significantly. The proposed method can be applied to all semantic segmentation tasks with a large number of spatially separate classes within an atlas-based prior.
Abstract (translated)
全脑分割需要推断大型图像卷积区中的数百个分割标签，因此对于深度学习方法来说，它带来了显著的实践挑战。我们引入了标签合并和分割方法，一种首先极大地减少了学习为基础的全脑分割所需的有效标签数量，然后恢复原始标签的方法。使用贪婪的图着色算法，我们的方法在模型训练和推理之前自动对多个空间上分离的标签进行分组和合并。合并的标签可能具有语义无关性。一个深度学习模型用于预测合并的标签。在推理时，使用基于解剖结构的感子区恢复原始标签。在我们的实验中，与不进行标签合并和分割的基线方法相比，将标签数量减少了68%，同时实现与基线方法相当的分割精度。此外，模型训练和推理时间以及GPU内存需求都显著减少。所提出的方法可以应用于所有具有大图卷积区中的大量空间上分离类别的语义分割任务。
URL
https://arxiv.org/abs/2404.10572
