# Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation

arXiv: https://arxiv.org/abs/2404.00667 · HTML: https://mlsanity.com/paper/2404.00667/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 58 references that are in the map.
- First posted on arXiv 31 Mar 2024.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1803.10464/) (2018)
- [What's the Point: Semantic Segmentation with Point Supervision](https://mlsanity.com/paper/1506.02106/) (2015)
- [Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1607.03516/) (2016)
- [Self-supervised Augmentation Consistency for Adapting Semantic Segmentation](https://mlsanity.com/paper/2105.00097/) (2021)
- [Micro-Net: A unified model for segmentation of various objects in microscopy images](https://mlsanity.com/paper/1804.08145/) (2018)
- [Domain Adaptive Semantic Segmentation Using Weak Labels](https://mlsanity.com/paper/2007.15176/) (2020)
- [Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images](https://mlsanity.com/paper/2007.05448/) (2020)
- [Cross-Domain Correlation Distillation for Unsupervised Domain Adaptation in Nighttime Semantic Segmentation](https://mlsanity.com/paper/2205.00858/) (2022)
- [Scribble-based Domain Adaptation via Co-segmentation](https://mlsanity.com/paper/2007.03632/) (2020)
- [Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation](https://mlsanity.com/paper/2103.04705/) (2021)
- [Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training](https://mlsanity.com/paper/1810.07911/) (2018)
- [ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2006.08658/) (2020)
- [Crowd counting via scale-adaptive convolutional neural network](https://mlsanity.com/paper/1711.04433/) (2017)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Domain Adaptive Segmentation in Volume Electron Microscopy Imaging](https://mlsanity.com/paper/1810.09734/) (2018)
- [WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy](https://mlsanity.com/paper/2210.13109/) (2022)
- [HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images](https://mlsanity.com/paper/2101.02877/) (2021)
- [Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision](https://mlsanity.com/paper/2108.08432/) (2021)
- [Exploiting Domain Transferability for Collaborative Inter-level Domain Adaptive Object Detection](https://mlsanity.com/paper/2207.09613/) (2022)

## Built on by (most cited first, 4 of 4)

- [Prompt-DAS: Annotation-Efficient Prompt Learning for Domain Adaptive Semantic Segmentation of Electron Microscopy Images](https://mlsanity.com/paper/2509.18973/) (2025)
- [Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy](https://mlsanity.com/paper/2510.16450/) (2025)
- [Prefer-DAS: Learning from Local Preferences and Sparse Prompts for Domain Adaptive Segmentation of Electron Microscopy](https://mlsanity.com/paper/2602.19423/) (2026)
- [μMatch: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM](https://mlsanity.com/paper/2606.21605/) (2026)

## Abstract (arXiv)

Accurate segmentation of organelle instances from electron microscopy (EM) images plays an essential role in many neuroscience researches. However, practical scenarios usually suffer from high annotation costs, label scarcity, and large domain diversity. While unsupervised domain adaptation (UDA) that assumes no annotation effort on the target data is promising to alleviate these challenges, its performance on complicated segmentation tasks is still far from practical usage. To address these issues, we investigate a highly annotation-efficient weak supervision, which assumes only sparse center-points on a small subset of object instances in the target training images. To achieve accurate segmentation with partial point annotations, we introduce instance counting and center detection as auxiliary tasks and design a multitask learning framework to leverage correlations among the counting, detection, and segmentation, which are all tasks with partial or no supervision. Building upon the different domain-invariances of the three tasks, we enforce counting estimation with a novel soft consistency loss as a global prior for center detection, which further guides the per-pixel segmentation. To further compensate for annotation sparsity, we develop a cross-position cut-and-paste for label augmentation and an entropy-based pseudo-label selection. The experimental results highlight that, by simply using extremely weak annotation, e.g., 15% sparse points, for model training, the proposed model is capable of significantly outperforming UDA methods and produces comparable performance as the supervised counterpart. The high robustness of our model shown in the validations and the low requirement of expert knowledge for sparse point annotation further improve the potential application value of our model.