# Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices

arXiv: https://arxiv.org/abs/2007.00833 · HTML: https://mlsanity.com/paper/2007.00833/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 16 of 16)

- [MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning](https://mlsanity.com/paper/2104.12166/) (2021)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation](https://mlsanity.com/paper/2208.09350/) (2022)
- [Clustering Propagation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2403.16646/) (2024)
- [Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss](https://mlsanity.com/paper/2102.01897/) (2021)
- [Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues](https://mlsanity.com/paper/2301.09914/) (2023)
- [Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces](https://mlsanity.com/paper/2110.12939/) (2021)
- [Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI](https://mlsanity.com/paper/2205.01675/) (2022)
- [Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO](https://mlsanity.com/paper/2406.19057/) (2024)
- [Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions](https://mlsanity.com/paper/2306.05912/) (2023)
- [Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation](https://mlsanity.com/paper/2409.03470/) (2024)
- [Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performance](https://mlsanity.com/paper/2603.29941/) (2026)
- [RIGS-Refiner: Risk-Guided Recursive Refinement in Prediction Space for Colonoscopy Polyp Segmentation](https://mlsanity.com/paper/2607.03058/) (2026)

## Abstract (arXiv)

Segmentation of the fetal brain from stacks of motion-corrupted fetal MRI slices is important for motion correction and high-resolution volume reconstruction. Although Convolutional Neural Networks (CNNs) have been widely used for automatic segmentation of the fetal brain, their results may still benefit from interactive refinement for challenging slices. To improve the efficiency of interactive refinement process, we propose an Uncertainty-Guided Interactive Refinement (UGIR) framework. We first propose a grouped convolution-based CNN to obtain multiple automatic segmentation predictions with uncertainty estimation in a single forward pass, then guide the user to provide interactions only in a subset of slices with the highest uncertainty. A novel interactive level set method is also proposed to obtain a refined result given the initial segmentation and user interactions. Experimental results show that: (1) our proposed CNN obtains uncertainty estimation in real time which correlates well with mis-segmentations, (2) the proposed interactive level set is effective and efficient for refinement, (3) UGIR obtains accurate refinement results with around 30% improvement of efficiency by using uncertainty to guide user interactions. Our code is available online.