  Automated quantitative measurement of the spine (i.e., multiple indices
estimation of heights, widths, areas, and so on for the vertebral body and
disc) is of the utmost importance in clinical spinal disease diagnoses, 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), which includes the CARN
architecture and local shape-constrained manifold regularization (LSCMR) loss
function, 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 consists of 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, thus
an expressive feature embedding is obtained. During training, the LSCMR is
utilized to alleviate overfitting and generate realistic estimation by learning
the multiple indices distribution. Experiments on MR images of 195 subjects
show that the proposed CARN achieves impressive performance with mean absolute
errors of 1.2496 mm, 1.2887 mm, and 1.2692 mm for estimation of 15 heights of
discs, 15 heights of vertebral bodies, and total indices respectively. The
proposed method has great potential in clinical spinal disease diagnoses.
