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

IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1–5

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 code-base is accessible to the public on GitHub.

Authors 6

1. Afshin Bozorgpour

   University of Regensburg

   Affiliation as printed

   University of Regensburg,Faculty of Informatics and Data Science,Germany

2. Bobby Azad

   South Dakota State University

   Affiliation as printed

   South Dakota State University,Electrical Engineering and Computer Science Department,USA

3. Reza Azad Aachen Faculty of Electrical Engineering and Information Technology

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Faculty of Electrical Engineering and Information Technology,Germany

4. Yury Velichko

   Northwestern University

   Affiliation as printed

   Machine and Hybrid Intelligence Lab, Northwestern University,Chicago,IL,USA

5. Ulaş Bağcı

   Northwestern University

   Affiliation as printed

   Machine and Hybrid Intelligence Lab, Northwestern University,Chicago,IL,USA

6. Dorit Merhof

   University of Regensburg

   Affiliation as printed

   University of Regensburg,Faculty of Informatics and Data Science,Germany