# VertMatch: A Semi-supervised Framework for Vertebral Structure Detection in 3D Ultrasound Volume

arXiv: https://arxiv.org/abs/2212.14747 · HTML: https://mlsanity.com/paper/2212.14747/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 52 references that are in the map.
- First posted on arXiv 28 Dec 2022.

## Builds on (most cited first, 16 of 16)

- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [A Simple Semi-Supervised Learning Framework for Object Detection](https://mlsanity.com/paper/2005.04757/) (2020)
- [Semi-supervised Medical Image Segmentation through Dual-task Consistency](https://mlsanity.com/paper/2009.04448/) (2020)
- [Semi-Supervised Semantic Segmentation with High- and Low-level Consistency](https://mlsanity.com/paper/1908.05724/) (2019)
- [Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework](https://mlsanity.com/paper/2103.11402/) (2021)
- [Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model](https://mlsanity.com/paper/2005.07377/) (2020)
- [FocalMix: Semi-Supervised Learning for 3D Medical Image Detection](https://mlsanity.com/paper/2003.09108/) (2020)
- [SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation](https://mlsanity.com/paper/2106.01544/) (2021)
- [Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization](https://mlsanity.com/paper/1705.05998/) (2017)
- [Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization](https://mlsanity.com/paper/2012.07947/) (2020)
- [Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information](https://mlsanity.com/paper/1812.03500/) (2018)

## Abstract (arXiv)

Three-dimensional (3D) ultrasound imaging technique has been applied for scoliosis assessment, but current assessment method only uses coronal projection image and cannot illustrate the 3D deformity and vertebra rotation. The vertebra detection is essential to reveal 3D spine information, but the detection task is challenging due to complex data and limited annotations. We propose VertMatch, a two-step framework to detect vertebral structures in 3D ultrasound volume by utilizing unlabeled data in semi-supervised manner. The first step is to detect the possible positions of structures on transverse slice globally, and then the local patches are cropped based on detected positions. The second step is to distinguish whether the patches contain real vertebral structures and screen the predicted positions from the first step. VertMatch develops three novel components for semi-supervised learning: for position detection in the first step, (1) anatomical prior is used to screen pseudo labels generated from confidence threshold method; (2) multi-slice consistency is used to utilize more unlabeled data by inputting multiple adjacent slices; (3) for patch identification in the second step, the categories are rebalanced in each batch to solve imbalance problem. Experimental results demonstrate that VertMatch can detect vertebra accurately in ultrasound volume and outperforms state-of-the-art methods. VertMatch is also validated in clinical application on forty ultrasound scans, and it can be a promising approach for 3D assessment of scoliosis.