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.

本文提出了一种名为 Seg4Reg+ 的多任务框架，通过优化分割和回归网络，探索了它们之间的局部和全局一致性以及知识迁移，并在公共数据集上进行了评估，证明了其在 Cobb 角估计方面的有效性和卓越表现。