# Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces

arXiv: https://arxiv.org/abs/2110.12939 · HTML: https://mlsanity.com/paper/2110.12939/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1604.05144/) (2016)
- [Deep Extreme Cut: From Extreme Points to Object Segmentation](https://mlsanity.com/paper/1711.09081/) (2017)
- [Deep Semantic Segmentation of Natural and Medical Images: A Review](https://mlsanity.com/paper/1910.07655/) (2019)
- [Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning](https://mlsanity.com/paper/1710.04043/) (2017)
- [DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks](https://mlsanity.com/paper/1605.07866/) (2016)
- [NiftyNet: a deep-learning platform for medical imaging](https://mlsanity.com/paper/1709.03485/) (2017)
- [Interactive segmentation of medical images through fully convolutional neural networks](https://mlsanity.com/paper/1903.08205/) (2019)
- [Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices](https://mlsanity.com/paper/2007.00833/) (2020)
- [Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network](https://mlsanity.com/paper/1712.06452/) (2017)

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

- [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023)
- [Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation](https://mlsanity.com/paper/2303.13696/) (2023)
- [B-Spine: Learning B-Spline Curve Representation for Robust and Interpretable Spinal Curvature Estimation](https://mlsanity.com/paper/2310.09603/) (2023)
- [DEEPBEAS3D: Deep Learning and B-Spline Explicit Active Surfaces](https://mlsanity.com/paper/2309.02335/) (2023)

## Abstract (arXiv)

Automatic medical image segmentation via convolutional neural networks (CNNs) has shown promising results. However, they may not always be robust enough for clinical use. Sub-optimal segmentation would require clinician's to manually delineate the target object, causing frustration. To address this problem, a novel interactive CNN-based segmentation framework is proposed in this work. The aim is to represent the CNN segmentation contour as B-splines by utilising B-spline explicit active surfaces (BEAS). The interactive element of the framework allows the user to precisely edit the contour in real-time, and by utilising BEAS it ensures the final contour is smooth and anatomically plausible. This framework was applied to the task of 2D segmentation of the levator hiatus from 2D ultrasound (US) images, and compared to the current clinical tools used in pelvic floor disorder clinic (4DView, GE Healthcare; Zipf, Austria). Experimental results show that: 1) the proposed framework is more robust than current state-of-the-art CNNs; 2) the perceived workload calculated via the NASA-TLX index was reduced more than half for the proposed approach in comparison to current clinical tools; and 3) the proposed tool requires at least 13 seconds less user time than the clinical tools, which was significant (p=0.001).