# Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression

arXiv: https://arxiv.org/abs/2208.12462 · HTML: https://mlsanity.com/paper/2208.12462/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation](https://mlsanity.com/paper/1805.04574/) (2018)
- [Robust Learning Through Cross-Task Consistency](https://mlsanity.com/paper/2006.04096/) (2020)
- [A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/1903.03313/) (2019)
- [Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/1506.04924/) (2015)
- [Vertebra-Focused Landmark Detection for Scoliosis Assessment](https://mlsanity.com/paper/2001.03187/) (2020)
- [Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression](https://mlsanity.com/paper/2012.12626/) (2020)

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

- [Few Shot Medical Image Segmentation with Cross Attention Transformer](https://mlsanity.com/paper/2303.13867/) (2023)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Image Quality-aware Diagnosis via Meta-knowledge Co-embedding](https://mlsanity.com/paper/2303.15038/) (2023)
- [Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation](https://mlsanity.com/paper/2305.00678/) (2023)
- [B-Spine: Learning B-Spline Curve Representation for Robust and Interpretable Spinal Curvature Estimation](https://mlsanity.com/paper/2310.09603/) (2023)
- [Rethinking Boundary Detection in Deep Learning-Based Medical Image Segmentation](https://mlsanity.com/paper/2505.04652/) (2025)
- [MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement](https://mlsanity.com/paper/2309.13817/) (2023)
- [Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentation](https://mlsanity.com/paper/2406.16189/) (2024)
- [Symmetric Perception and Ordinal Regression for Detecting Scoliosis Natural Image](https://mlsanity.com/paper/2411.15799/) (2024)
- [A New Angle on Bones: Robust Pose Estimation in X-Ray and Ultrasound](https://mlsanity.com/paper/2606.04700/) (2026)
- [Predicting the Progression of Adolescent Idiopathic Scoliosis](https://mlsanity.com/paper/2609.28434/) (2026)

## Abstract (arXiv)

Automated methods for Cobb angle estimation are of high demand for scoliosis assessment. Existing methods typically calculate the Cobb angle from landmark estimation, or simply combine the low-level task (e.g., landmark detection and spine segmentation) with the Cobb angle regression task, without fully exploring the benefits from each other. In this study, we propose a novel multi-task framework, named Seg4Reg+, which jointly optimizes the segmentation and regression networks. We thoroughly investigate both local and global consistency and knowledge transfer between each other. Specifically, we propose an attention regularization module leveraging class activation maps (CAMs) from image-segmentation pairs to discover additional supervision in the regression network, and the CAMs can serve as a region-of-interest enhancement gate to facilitate the segmentation task in turn. Meanwhile, we design a novel triangle consistency learning to train the two networks jointly for global optimization. The evaluations performed on the public AASCE Challenge dataset demonstrate the effectiveness of each module and superior performance of our model to the state-of-the-art methods.