CAB U-Net: An end-to-end category attention boosting algorithm for segmentation. (September 2020)

• Record Type: Journal Article
• Authors: Ding, Xiaofeng; Peng, Yaxin; Shen, Chaomin; Zeng, Tieyong
• Is Part Of: Computerized medical imaging and graphics. Volume 84(2020)
• Publication Date: 2020-09
• Subjects: Segmentation -- Boosting -- Category attention; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2020.101764
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: CAB-UNet: an end-to-end Category Attention Boosting algorithm. A gradient boosting and deep learning method for 3D medical image segmentation. Verified on HVSMR 2016 and MM-WHS 2017 Challenge datasets. Outperform the state-of-the-art algorithms. Abstract: With the development of machine learning and artificial intelligence, many convolutional neural networks (CNNs) based segmentation methods have been proposed for 3D cardiac segmentation. In this paper, we propose the category attention boosting (CAB) module, which combines the deep network calculation graph with the boosting method. On the one hand, we add the attention mechanism into the gradient boosting process, which enhances the information of coarse segmentation without high computation cost. On the other hand, we introduce the CAB module into the 3D U-Net segmentation network and construct a new multi-scale boosting model CAB U-Net which strengthens the gradient flow in the network and makes full use of the low resolution feature information. Thanks to the advantage that end-to-end networks can adaptively adjust the internal parameters, CAB U-Net can make full use of the complementary effects among different base learners. Extensive experiments on public datasets show that our approach can achieve superior performance over the state-of-the-art methods.