# MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization

arXiv: https://arxiv.org/abs/2303.12649 · HTML: https://mlsanity.com/paper/2303.12649/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018)
- [Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images](https://mlsanity.com/paper/1808.08114/) (2018)
- [Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation](https://mlsanity.com/paper/2002.02255/) (2020)
- [Domain Generalization by Mutual-Information Regularization with Pre-trained Models](https://mlsanity.com/paper/2203.10789/) (2022)
- [Domain Agnostic Learning with Disentangled Representations](https://mlsanity.com/paper/1904.12347/) (2019)
- [Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging](https://mlsanity.com/paper/2011.00739/) (2020)
- [Rethinking Ultrasound Augmentation: A Physics-Inspired Approach](https://mlsanity.com/paper/2105.02188/) (2021)
- [A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework](https://mlsanity.com/paper/2108.07979/) (2021)
- [Online Reflective Learning for Robust Medical Image Segmentation](https://mlsanity.com/paper/2207.00476/) (2022)
- [CACTUSS: Common Anatomical CT-US Space for US examinations](https://mlsanity.com/paper/2207.08619/) (2022)

## Built on by (most cited first, 18 of 18)

- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations](https://mlsanity.com/paper/2307.03705/) (2023)
- [Diffusion as Sound Propagation: Physics-inspired Model for Ultrasound Image Generation](https://mlsanity.com/paper/2407.05428/) (2024)
- [U2-BENCH: Benchmarking Large Vision-Language Models on Ultrasound Understanding](https://mlsanity.com/paper/2505.17779/) (2025)
- [A Survey on Domain Generalization for Medical Image Analysis](https://mlsanity.com/paper/2402.05035/) (2024)
- [Speckle2Self: Self-Supervised Ultrasound Speckle Reduction Without Clean Data](https://mlsanity.com/paper/2507.06828/) (2025)
- [DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler Signal](https://mlsanity.com/paper/2305.08938/) (2023)
- [VibNet: Vibration-Boosted Needle Detection in Ultrasound Images](https://mlsanity.com/paper/2403.14523/) (2024)
- [CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation](https://mlsanity.com/paper/2411.10136/) (2024)
- [Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images](https://mlsanity.com/paper/2411.04004/) (2024)
- [UMind-VL: A Generalist Ultrasound Vision-Language Model for Unified Grounded Perception and Comprehensive Interpretation](https://mlsanity.com/paper/2511.22256/) (2025)
- [Deep Spectral Methods for Unsupervised Ultrasound Image Interpretation](https://mlsanity.com/paper/2408.02043/) (2024)
- [Ultrasound-Guided Real-Time Spinal Motion Visualization for Spinal Instability Assessment](https://mlsanity.com/paper/2602.12917/) (2026)
- [UltraSAM3: A Concept-Driven Foundation Model for Universal Ultrasound Image Segmentation](https://mlsanity.com/paper/2607.29200/) (2026)
- [UltraG-Bench: A Multi-task Benchmark for assessing Large Vision-Language Models on Pixel-level Evidence Grounding in Ultrasound](https://mlsanity.com/paper/2609.30928/) (2026)
- [Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation](https://mlsanity.com/paper/2507.23326/) (2025)
- [Self-Supervised Multi-Stage Domain Unlearning for White-Matter Lesion Segmentation](https://mlsanity.com/paper/2603.13328/) (2026)
- [Geometry-aware Prototype Learning for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2605.10885/) (2026)

## Abstract (arXiv)

Generalization capabilities of learning-based medical image segmentation across domains are currently limited by the performance degradation caused by the domain shift, particularly for ultrasound (US) imaging. The quality of US images heavily relies on carefully tuned acoustic parameters, which vary across sonographers, machines, and settings. To improve the generalizability on US images across domains, we propose MI-SegNet, a novel mutual information (MI) based framework to explicitly disentangle the anatomical and domain feature representations; therefore, robust domain-independent segmentation can be expected. Two encoders are employed to extract the relevant features for the disentanglement. The segmentation only uses the anatomical feature map for its prediction. In order to force the encoders to learn meaningful feature representations a cross-reconstruction method is used during training. Transformations, specific to either domain or anatomy are applied to guide the encoders in their respective feature extraction task. Additionally, any MI present in both feature maps is punished to further promote separate feature spaces. We validate the generalizability of the proposed domain-independent segmentation approach on several datasets with varying parameters and machines. Furthermore, we demonstrate the effectiveness of the proposed MI-SegNet serving as a pre-trained model by comparing it with state-of-the-art networks.