Abstract
Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation. Compared with 2D images, 3D medical volumes involve information from different directions, e.g., transverse, sagittal, and coronal planes, so as to naturally provide complementary views. These complementary views and the intrinsic similarity among adjacent 3D slices inspire us to develop a novel annotation way and its corresponding semi-supervised model for effective segmentation. Specifically, we firstly propose the orthogonal annotation by only labeling two orthogonal slices in a labeled volume, which significantly relieves the burden of annotation. Then, we perform registration to obtain the initial pseudo labels for sparsely labeled volumes. Subsequently, by introducing unlabeled volumes, we propose a dual-network paradigm named Dense-Sparse Co-training (DeSCO) that exploits dense pseudo labels in early stage and sparse labels in later stage and meanwhile forces consistent output of two networks. Experimental results on three benchmark datasets validated our effectiveness in performance and efficiency in annotation. For example, with only 10 annotated slices, our method reaches a Dice up to 86.93% on KiTS19 dataset.
Abstract (translated)
最近的趋势是半监督学习，这极大地提高了3D半监督医学图像分割的性能。相比2D图像，3D医疗体积从不同方向涉及信息，例如横断、 sagittal和 coronal平面，以自然提供互补观点。这些互补观点和相邻3D切片之间的内在相似性启发我们开发一种新的标注方式和相应的半监督模型，以有效地分割。具体来说，我们首先提出了垂直标注，仅在每个标记体积中标记两个垂直切片，从而显著减轻了标注的负担。随后，我们进行注册以获取较少标记的初始伪标签。随后，通过引入未标记体积，我们提出了一种名为Dense-Sparse Co-training(DeSCO)的双网络范式，该范式在早期利用密集伪标签，而在后期利用稀疏标签，同时强制两个网络的一致性输出。对三个基准数据集的实验结果验证了我们的性能效率和标注的有效性。例如，仅使用10个标注切片，我们的方法在Kinets19数据集上达到Dice高达86.93%。
URL
https://arxiv.org/abs/2303.13090
