ACMTR: Attention-guided, combined multi-scale, transformer reasoning-based network for 3D CT pelvic functional bone marrow segmentation. (April 2023)

• Record Type: Journal Article
• Authors: Zeng, Junying; Li, Wanying; Zheng, Bin; Xiao, Lin; Zhang, Xiuping; Zhong, Qian; Liang, Shufen; Wang, Jie; Huang, Yuanqiong; Qin, Chuanbo
• Is Part Of: Biomedical signal processing and control. Volume 82(2023)
• Publication Date: 2023-04
• Subjects: CT dataset -- Pelvic segmentation -- Deep learning -- Transformer -- Deformable Attention -- Feature extraction -- Global Reasoning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104522
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Automatic segmentation of pelvic functional bone marrow (FBM) in computed tomography (CT) plays a critical role in the accurate diagnosis of pelvic malignancies and treatment planning for patients. Pelvic bone marrow near the mid-axis of the body, i.e., the FBM, is the most hematopoietic. Here we propose an A ttention-guided, C ombined M ulti-scale, Transformer Reasoning -based network (ACMTR) for accurate segmentation of pelvic FBM for intensity-modulated radiation therapy. The presented Convolution-Attention Fusion Encoder overcomes the limited receptive field of convolution as well as the class imbalance issue in pelvic FBM segmentation. We also propose a Multi-scale Transformer Global Reasoning Bottleneck module for obtaining multi-scale, global attention features for extraction of small edge tissue of the FBM. We introduce a deformable attention mechanism at the decoding path to focus on key features instead of redundant information with a minimal increase in computational complexity. The pelvic FBM CT images used in this study were from a self-constructed clinical database of 92 cases and showed two main areas of FBM: the most intense FBM area (i.e., most hematopoietic area) and the second most intense FBM area. The proposed ACMTR was compared with the popular deep learning models U-Net, VGG-Net, Attention-UNet, TransUNet, CoTr, nnUNet, and nnFormer. For the most intense and second most intense FBM areas, the proposed ACMTR achieved Dice Scores of 0.799 and 0.772 and Hausdorff Distance of 6.061 and 4.791, respectively, on test data. The implementation code is available at the following Github link: https://github.com/walynlee/ACMTR .