Direct automated quantitative measurement of spine by cascade amplifier regression network with manifold regularization

• Published in: Medical image analysis Vol. 55; pp. 103 - 115
• Main Authors: Pang, Shumao, Su, Zhihai, Leung, Stephanie, Nachum, Ilanit Ben, Chen, Bo, Feng, Qianjin, Li, Shuo
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2019; Elsevier BV
• Subjects: Amplifiers; Assessments; Automation; Biocompatibility; Biomedical materials; Deep learning; Degeneration; Embedding; Feature maps; Intervertebral discs; Local linear representation; Manifold learning; Osteoporosis; Regression models; Regularization; Spine; Spine (lumbar); Vertebrae; Deep learning; Local linear representation; Manifold learning; Spine
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2019.04.012

•A novel regression network named CARN is proposed to achieve automated quantitative measurement of the spine, which provides a reliable measurement for the clinical diagnosis and assessment of spinal diseases.•The local structure-preserved manifold regularization (LSPMR) is proposed to generate discriminative feature embedding, which largely improves the performance of multiple indices estimation.•The adaptive local shape-constrained manifold regularization (ALSCMR) is proposed to alleviate overfitting. This provides a novel approach for multi-output regression to improve the generalization of the multi-output regression network. [Display omitted] Automated quantitative measurement of the spine (i.e., multiple indices estimation of heights, widths, areas, and so on for the vertebral body and disc) plays a significant role in clinical spinal disease diagnoses and assessments, such as osteoporosis, intervertebral disc degeneration, and lumbar disc herniation, yet still an unprecedented challenge due to the variety of spine structure and the high dimensionality of indices to be estimated. In this paper, we propose a novel cascade amplifier regression network (CARN) with manifold regularization including local structure-preserved manifold regularization (LSPMR) and adaptive local shape-constrained manifold regularization (ALSCMR), to achieve accurate direct automated multiple indices estimation. The CARN architecture is composed of a cascade amplifier network (CAN) for expressive feature embedding and a linear regression model for multiple indices estimation. The CAN produces an expressive feature embedding by cascade amplifier units (AUs), which are used for selective feature reuse by stimulating effective feature and suppressing redundant feature during propagating feature map between adjacent layers. During training, the LSPMR is employed to obtain discriminative feature embedding by preserving the local geometric structure of the latent feature space similar to the target output manifold. The ALSCMR is utilized to alleviate overfitting and generate realistic estimation by learning the multiple indices distribution. Experiments on T1-weighted MR images of 215 subjects and T2-weighted MR images of 20 subjects show that the proposed approach achieves impressive performance with mean absolute errors of 1.22 ± 1.04 mm and 1.24 ± 1.07 mm for the 30 lumbar spinal indices estimation of the T1-weighted and T2-weighted spinal MR images respectively. The proposed method has great potential in clinical spinal disease diagnoses and assessments.