Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or IVD herniation. We present HCA-Net, a novel contextual attention network architecture for semantic labeling of IVDs, with a special focus on exploiting prior geometric information. Our approach excels at processing features across different scales and effectively consolidating them to capture the intricate spatial relationships within the spinal cord. To achieve this, HCA-Net models IVD labeling as a pose estimation problem, aiming to minimize the discrepancy between each predicted IVD location and its corresponding actual joint location. In addition, we introduce a skeletal loss term to reinforce the model's geometric dependence on the spine. This loss function is designed to constrain the model's predictions to a range that matches the general structure of the human vertebral skeleton. As a result, the network learns to reduce the occurrence of false predictions and adaptively improves the accuracy of IVD location estimation. Through extensive experimental evaluation on multi-center spine datasets, our approach consistently outperforms previous state-of-the-art methods on both MRI T1w and T2w modalities. The codebase is accessible to the public on \href{https://github.com/xmindflow/HCA-Net}{GitHub}.

翻译：医学图像中椎间盘的精确自动化分割对于评估脊柱相关疾病（如骨质疏松、椎体骨折或椎间盘突出）至关重要。我们提出了HCA-Net，一种新颖的上下文注意力网络架构，专门用于椎间盘语义标注，重点是利用先验几何信息。该方法擅长处理不同尺度的特征，并有效整合这些特征以捕捉脊髓内复杂的空间关系。为此，HCA-Net将椎间盘标注建模为姿态估计问题，旨在最小化每个预测椎间盘位置与其对应实际关节位置之间的差异。此外，我们引入了一种骨骼损失项以增强模型对脊柱几何结构的依赖性。该损失函数约束模型预测结果符合人体椎骨骨架的一般结构范围。因此，网络能够学习减少误判，并自适应提升椎间盘位置估计的准确性。通过在多个多中心脊柱数据集上的广泛实验评估，我们的方法在MRI T1w和T2w两种模态下均持续优于现有最先进方法。代码库已公开于\href{https://github.com/xmindflow/HCA-Net}{GitHub}。