Background: Deep learning has presented great potential in accurate MR image
segmentation when enough labeled data are provided for network optimization.
However, manually annotating 3D MR images is tedious and time-consuming,
requiring experts with rich domain knowledge and experience. Purpose: To build
a deep learning method exploring sparse annotations, namely only a single 2D
slice label for each 3D training MR image. Population: 3D MR images of 150
subjects from two publicly available datasets were included. Among them, 50
(1,377 image slices) are for prostate segmentation. The other 100 (8,800 image
slices) are for left atrium segmentation. Five-fold cross-validation
experiments were carried out utilizing the first dataset. For the second
dataset, 80 subjects were used for training and 20 were used for testing.
Assessment: A collaborative learning method by integrating the strengths of
semi-supervised and self-supervised learning schemes was developed. The method
was trained using labeled central slices and unlabeled non-central slices.
Segmentation performance on testing set was reported quantitatively and
qualitatively. Results: Compared to FS-LCS, MT, UA-MT, DCT-Seg, ICT, and AC-MT,
the proposed method achieved a substantial improvement in segmentation
accuracy, increasing the mean B-IoU significantly by more than 10.0% for
prostate segmentation (proposed method B-IoU: 70.3% vs. ICT B-IoU: 60.3%) and
by more than 6.0% for left atrium segmentation (proposed method B-IoU: 66.1%
vs. ICT B-IoU: 60.1%).

建立了一种深度学习方法，探索了稀疏标注，即每个 3D 训练 MR 图像只有一个 2D 切片进行标注，与 ICT 相比，该方法在分割准确性方面取得了显著提升，对于前列腺分割，平均 B-IoU 增加了 10.0% 以上（提出的方法 B-IoU：70.3% vs. ICT B-IoU：60.3%），对于左心房分割，平均 B-IoU 增加了 6.0% 以上（提出的方法 B-IoU：66.1% vs. ICT B-IoU：60.1%）。