  Instance segmentation in 3D images is a fundamental task in biomedical image
analysis. While deep learning models often work well for 2D instance
segmentation, 3D instance segmentation still faces critical challenges, such as
insufficient training data due to various annotation difficulties in 3D
biomedical images. Common 3D annotation methods (e.g., full voxel annotation)
incur high workloads and costs for labeling enough instances for training deep
learning 3D instance segmentation models. In this paper, we propose a new weak
annotation approach for training a fast deep learning 3D instance segmentation
model without using full voxel mask annotation. Our approach needs only 3D
bounding boxes for all instances and full voxel annotation for a small fraction
of the instances, and uses a novel two-stage 3D instance segmentation model
utilizing these two kinds of annotation, respectively. We evaluate our approach
on several biomedical image datasets, and the experimental results show that
(1) with full annotated boxes and a small amount of masks, our approach can
achieve similar performance as the best known methods using full annotation,
and (2) with similar annotation time, our approach outperforms the best known
methods that use full annotation.
