DoubleU-Net++: Architecture with Exploit Multiscale Features for Vertebrae Segmentation

TL;DR

DoubleU-Net++ is a novel deep learning architecture that leverages multiscale features, attention modules, and multiple views to significantly improve vertebrae segmentation accuracy and speed in medical imaging.

Contribution

The paper introduces DoubleU-Net++, a new model combining DenseNet, CBAM, and PSA modules for enhanced multiview vertebrae segmentation, outperforming existing methods.

Findings

Achieved over 94% accuracy on VerSe2020 dataset across views.

Improved segmentation precision, recall, and F1-score by 4-6%.

Trained faster than state-of-the-art models.

Abstract

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.