HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling

TL;DR

HCA-Net is a novel neural network architecture that leverages hierarchical context and geometric priors for accurate, automated segmentation of intervertebral discs in medical images, improving over previous methods.

Contribution

The paper introduces HCA-Net, which models IVD labeling as a pose estimation problem with a skeletal loss, enhancing accuracy by incorporating geometric spine information.

Findings

Outperforms previous state-of-the-art methods on multi-center datasets

Effective across MRI T1w and T2w modalities

Reduces false predictions through geometric constraints

Abstract

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}.