Volumetric magnetic resonance (MR) image segmentation plays an important role in many clinical applications. Deep learning (DL) has recently achieved state-of-the-art or even human-level performance on various image segmentation tasks. Nevertheless, manually annotating volumetric MR images for DL model training is labor-exhaustive and time-consuming. In this work, we aim to train a semi-supervised and self-supervised collaborative learning framework for prostate 3D MR image segmentation while using extremely sparse annotations, for which the ground truth annotations are provided for just the central slice of each volumetric MR image. Specifically, semi-supervised learning and self-supervised learning methods are used to generate two independent sets of pseudo labels. These pseudo labels are then fused by Boolean operation to extract a more confident pseudo label set. The images with either manual or network self-generated labels are then employed to train a segmentation model for target volume extraction. Experimental results on a publicly available prostate MR image dataset demonstrate that, while requiring significantly less annotation effort, our framework generates very encouraging segmentation results. The proposed framework is very useful in clinical applications when training data with dense annotations are difficult to obtain.
翻译:深度学习(DL)最近在各种图像分割任务中取得了最先进的甚至人类一级的表现。尽管如此,为 DL 模式培训人工批注体积MR 图像,是劳动力的穷尽和耗时。在这项工作中,我们的目标是为前列腺 3D MR 图像分割培训一个半监督和自我监督的合作学习框架,同时使用极为稀少的注释,为每个量子MR 图像的中央切片提供地面真相说明。具体地说,使用半监督的学习和自我监督的学习方法来生成两套独立的假标签。这些假标签随后由Boolean操作结合,以提取一个更自信的假标签。随后,使用手动或网络自制标签的图像来为目标量提取培训一个分解模型。公共提供的前列腺MR图像数据集的实验结果显示,在不需要大量说明的情况下,我们的框架生成了非常令人振奋的分解结果。拟议的框架在临床培训中获得非常有用的数据。