# DEEPBEAS3D: Deep Learning and B-Spline Explicit Active Surfaces

arXiv: https://arxiv.org/abs/2309.02335 · HTML: https://mlsanity.com/paper/2309.02335/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation](https://mlsanity.com/paper/1707.00652/) (2017)
- [MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning](https://mlsanity.com/paper/2104.12166/) (2021)
- [ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation](https://mlsanity.com/paper/2201.04584/) (2022)
- [Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces](https://mlsanity.com/paper/2110.12939/) (2021)
- [NASA-TLX Web App: An Online Tool to Analyse Subjective Workload](https://mlsanity.com/paper/2001.09963/) (2020)

## Abstract (arXiv)

Deep learning-based automatic segmentation methods have become state-of-the-art. However, they are often not robust enough for direct clinical application, as domain shifts between training and testing data affect their performance. Failure in automatic segmentation can cause sub-optimal results that require correction. To address these problems, we propose a novel 3D extension of an interactive segmentation framework that represents a segmentation from a convolutional neural network (CNN) as a B-spline explicit active surface (BEAS). BEAS ensures segmentations are smooth in 3D space, increasing anatomical plausibility, while allowing the user to precisely edit the 3D surface. We apply this framework to the task of 3D segmentation of the anal sphincter complex (AS) from transperineal ultrasound (TPUS) images, and compare it to the clinical tool used in the pelvic floor disorder clinic (4D View VOCAL, GE Healthcare; Zipf, Austria). Experimental results show that: 1) the proposed framework gives the user explicit control of the surface contour; 2) the perceived workload calculated via the NASA-TLX index was reduced by 30% compared to VOCAL; and 3) it required 7 0% (170 seconds) less user time than VOCAL (p< 0.00001)