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

arXiv: https://arxiv.org/abs/2201.12389 · HTML: https://mlsanity.com/paper/2201.12389/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [Iterative fully convolutional neural networks for automatic vertebra segmentation and identification](https://mlsanity.com/paper/1804.04383/) (2018)
- [A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets](https://mlsanity.com/paper/1703.04347/) (2017)
- [Whole Brain Segmentation with Full Volume Neural Network](https://mlsanity.com/paper/2110.15601/) (2021)
- [U-Net Based Architecture for an Improved Multiresolution Segmentation in Medical Images](https://mlsanity.com/paper/2007.08238/) (2020)

## Abstract (arXiv)

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.