# Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation

arXiv: https://arxiv.org/abs/1806.11137 · HTML: https://mlsanity.com/paper/1806.11137/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2018.

## Built on by (most cited first, 11 of 11)

- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training](https://mlsanity.com/paper/2202.08195/) (2022)
- [AnyStar: Domain randomized universal star-convex 3D instance segmentation](https://mlsanity.com/paper/2307.07044/) (2023)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Non-Iterative Scribble-Supervised Learning with Pacing Pseudo-Masks for Medical Image Segmentation](https://mlsanity.com/paper/2210.10956/) (2022)
- [ParticleSeg3D: A Scalable Out-of-the-Box Deep Learning Segmentation Solution for Individual Particle Characterization from Micro CT Images in Mineral Processing and Recycling](https://mlsanity.com/paper/2301.13319/) (2023)
- [Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing](https://mlsanity.com/paper/2304.10671/) (2023)
- [Cell Tracking-by-detection using Elliptical Bounding Boxes](https://mlsanity.com/paper/2310.04895/) (2023)
- [BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks](https://mlsanity.com/paper/2206.04558/) (2022)
- [Rethinking Barely-Supervised Volumetric Medical Image Segmentation from an Unsupervised Domain Adaptation Perspective](https://mlsanity.com/paper/2405.09777/) (2024)
- [SP 3 : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation](https://mlsanity.com/paper/2411.11636/) (2024)

## Abstract (arXiv)

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.