# Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior

arXiv: https://arxiv.org/abs/1804.01307 · HTML: https://mlsanity.com/paper/1804.01307/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2018.

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

- [A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data](https://mlsanity.com/paper/2103.06360/) (2021)
- [SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation](https://mlsanity.com/paper/2402.16368/) (2024)
- [Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle](https://mlsanity.com/paper/2110.12177/) (2021)
- [Multi-View Vertebra Localization and Identification from CT Images](https://mlsanity.com/paper/2307.12845/) (2023)
- [Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning](https://mlsanity.com/paper/2103.07279/) (2021)
- [Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine](https://mlsanity.com/paper/2205.07568/) (2022)
- [SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation](https://mlsanity.com/paper/2407.08555/) (2024)
- [A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning](https://mlsanity.com/paper/2203.00624/) (2022)
- [VertDetect: Fully End-to-End 3D Vertebral Instance Segmentation Model](https://mlsanity.com/paper/2311.09958/) (2023)
- [Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort](https://mlsanity.com/paper/2505.05004/) (2025)
- [Attend-and-Refine: Interactive keypoint estimation and quantitative cervical vertebrae analysis for bone age assessment](https://mlsanity.com/paper/2507.07670/) (2025)

## Abstract (arXiv)

Robust localisation and identification of vertebrae is essential for automated spine analysis. The contribution of this work to the task is two-fold: (1) Inspired by the human expert, we hypothesise that a sagittal and coronal reformation of the spine contain sufficient information for labelling the vertebrae. Thereby, we propose a butterfly-shaped network architecture (termed Btrfly Net) that efficiently combines the information across reformations. (2) Underpinning the Btrfly net, we present an energy-based adversarial training regime that encodes local spine structure as an anatomical prior into the network, thereby enabling it to achieve state-of-art performance in all standard metrics on a benchmark dataset of 302 scans without any post-processing during inference.