# Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation

arXiv: https://arxiv.org/abs/2107.02854 · HTML: https://mlsanity.com/paper/2107.02854/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2021.

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

- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [A Graph-theoretic Algorithm for Small Bowel Path Tracking in CT Scans](https://mlsanity.com/paper/2110.00466/) (2021)
- [Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations](https://mlsanity.com/paper/2206.14847/) (2022)
- [Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor](https://mlsanity.com/paper/2207.14700/) (2022)
- [Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation](https://mlsanity.com/paper/2305.06978/) (2023)
- [Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation](https://mlsanity.com/paper/2205.05898/) (2022)
- [Graph-Based Small Bowel Path Tracking with Cylindrical Constraints](https://mlsanity.com/paper/2207.14436/) (2022)
- [Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data](https://mlsanity.com/paper/2308.15881/) (2023)
- [Day-Night Adaptation: An Innovative Source-free Adaptation Framework for Medical Image Segmentation](https://mlsanity.com/paper/2410.13472/) (2024)

## Abstract (arXiv)

We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement. To make the domain adaptation more controllable, we disentangle intensity and non-intensity features within a unique two-stream auto-encoding architecture, and selectively adapt the non-intensity features that are believed to be more transferable across domains. The segmentation prediction is performed by aggregating the disentangled features. We evaluated our method using intravenous contrast-enhanced abdominal CT scans with and without oral contrast, which are used as source and target domains, respectively. The proposed method showed clear improvements in terms of three different metrics compared to other domain adaptation methods that are without the feature disentanglement. The method brings small bowel segmentation closer to clinical application.