  Accurate segmentation of the vertebra is an important prerequisite in various
medical applications (E.g. tele surgery) to assist surgeons. Following the
successful development of deep neural networks, recent studies have focused on
the essential rule of vertebral segmentation. Prior works contain a large
number of parameters, and their segmentation is restricted to only one view.
Inspired by DoubleU-Net, we propose a novel model named DoubleU-Net++ in which
DensNet as feature extractor, special attention module from Convolutional Block
Attention on Module (CBAM) and, Pyramid Squeeze Attention (PSA) module are
employed to improve extracted features. We evaluate our proposed model on three
different views (sagittal, coronal, and axial) of VerSe2020 and xVertSeg
datasets. Compared with state-of-the-art studies, our architecture is trained
faster and achieves higher precision, recall, and F1-score as evaluation
(imporoved by 4-6%) and the result of above 94% for sagittal view and above 94%
for both coronal view and above 93% axial view were gained for VerSe2020
dataset, respectively. Also, for xVertSeg dataset, we achieved precision,
recall,and F1-score of above 97% for sagittal view, above 93% for coronal view
,and above 96% for axial view.
